U.S. patent application number 13/942139 was filed with the patent office on 2014-12-11 for method, apparatus, and computer program product for monitoring health, fitness, operation, or performance of individuals.
This patent application is currently assigned to ZIH Corp.. The applicant listed for this patent is ZIH Corp.. Invention is credited to James J. O'Hagan, Wolfgang Strobel, Michael A. Wohl.
Application Number | 20140364973 13/942139 |
Document ID | / |
Family ID | 52004990 |
Filed Date | 2014-12-11 |
United States Patent
Application |
20140364973 |
Kind Code |
A1 |
O'Hagan; James J. ; et
al. |
December 11, 2014 |
METHOD, APPARATUS, AND COMPUTER PROGRAM PRODUCT FOR MONITORING
HEALTH, FITNESS, OPERATION, OR PERFORMANCE OF INDIVIDUALS
Abstract
Provided herein are systems, methods and computer readable media
for monitoring the health and fitness of an individual. An example
method comprises correlating a tag and a sensor to the individual,
receiving tag derived data indicative of a location for the
individual, and receiving sensor derived data indicative of at
least one of a health, a fitness, an operation level, or a
performance level for the individual. The method further comprises
comparing the tag location data of the tag derived data to
individual dynamics/kinetics models and comparing the sensor
derived data to at least one of health models, fitness models,
operation level models, or performance level models. A HFOP status
is then determined for the individual based on the comparing the
tag location data to individual dynamics/kinetics models and on the
comparing the sensor derived data to at least one of health models,
fitness models, operation level models, or performance level
models.
Inventors: |
O'Hagan; James J.;
(Lincolnshire, IL) ; Strobel; Wolfgang; (Lincoln,
RI) ; Wohl; Michael A.; (Rogersville, TN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
ZIH Corp. |
Lincolnshire |
IL |
US |
|
|
Assignee: |
ZIH Corp.
Lincolnshire
IL
|
Family ID: |
52004990 |
Appl. No.: |
13/942139 |
Filed: |
July 15, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61831990 |
Jun 6, 2013 |
|
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|
Current U.S.
Class: |
700/91 |
Current CPC
Class: |
G06N 5/02 20130101; H04Q
9/00 20130101; A63B 2024/0025 20130101; G06F 16/955 20190101; G08C
17/02 20130101; A41D 1/002 20130101; H04W 4/029 20180201; G06Q
90/00 20130101; A63B 24/0021 20130101; A41D 1/04 20130101; H04B
1/719 20130101; A63B 71/0686 20130101; A63B 2220/40 20130101; H04L
43/04 20130101; A63B 2024/0028 20130101; A63B 2024/0056 20130101;
A63B 2220/12 20130101; A42B 3/30 20130101; A63B 2220/836 20130101;
A63B 2225/50 20130101; G06K 9/00342 20130101; G05B 15/02 20130101;
G06K 7/10306 20130101; G06N 7/005 20130101; G16H 40/67 20180101;
G16H 50/30 20180101; G16H 50/50 20180101; H04B 1/71635 20130101;
A41D 1/005 20130101; A63B 2225/54 20130101; H04B 1/71637 20130101;
G06K 7/10297 20130101; G06K 7/10366 20130101; G06F 16/9537
20190101; H04B 1/1036 20130101; H04W 4/02 20130101; G06F 16/951
20190101; G06K 7/10227 20130101; H04B 1/7097 20130101; G06F 16/9554
20190101; G09B 19/0038 20130101; A63B 24/00 20130101; A41D 2600/10
20130101 |
Class at
Publication: |
700/91 |
International
Class: |
G06K 7/10 20060101
G06K007/10 |
Claims
1. A method for monitoring an individual, the method comprising:
receiving tag derived data comprising tag location data and blink
data, wherein the tag location data is determined based at least in
part on the blink data; receiving sensor derived data indicative of
at least one of a health, a fitness, an operation level, or a
performance level for the individual; comparing the tag location
data to individual dynamics/kinetics models; comparing the sensor
derived data to at least one of health models, fitness models,
operation level models, or performance level models; and
determining a health, fitness, operation and performance (HFOP)
status for the individual based on the comparing the tag location
data to individual dynamics/kinetics models and on the comparing
the sensor derived data to at least one of health models, fitness
models, operation level models, or performance level models.
2. The method according to claim 1 further comprising: determining
whether the HFOP status satisfies a threshold value and generating,
in an instance in which the HFOP status does satisfy the threshold
value, an alert.
3. The method according to claim 1, wherein the blink data is
generated by one or more tags comprising an ultra-wideband (UWB)
transmitter.
4. The method according to claim 3, wherein the UWB transmitter is
configured to transmit a plurality of time of arrival (TOA) timing
pulses.
5. The method according to claim 3, wherein the UWB transmitter is
configured to transmit a tag data packet comprising 112 bits.
6. The method according to claim 1, wherein the tag derived data
further comprises a tag UID and a tag-individual correlator.
7. The method according to claim 1, wherein the tag derived data
further comprises tag data, a tag UID, a tag-sensor correlator, and
a sensor information packet.
8. The method according to claim 1, wherein the sensor derived data
comprises a sensor UID and additional stored sensor data.
9. The method according to claim 1, wherein the sensor derived data
comprises a sensor UID, additional stored sensor data, a
sensor-individual correlator, and environmental measurements.
10. The method according to claim 1, wherein the sensor derived
data comprises a position calculation determined by a triangulation
positioner.
11. The method according to claim 1, wherein the sensor derived
data comprises position information read from a proximity
label.
12. The method according to claim 1, wherein the sensor derived
data comprises associated sensor data generated by a sensor
receiver upon receiving sensor information packets from first and
second sensors.
13. The method according to claim 1, wherein at least part of the
sensor derived data was transmitted from a location tag as part of
a tag data packet.
14. The method according to claim 1, wherein the sensor derived
data comprises environmental measurements generated by one or more
sensors comprising at least one of an accelerometer, a diagnostic
device, a triangulation positioner, or a proximity positioner.
15. The method according to claim 1, wherein the individual is a
human being and the sensor derived data is generated from one or
more sensors comprising at least one of a blood pressure sensor, a
heart rate sensor, a body temperature sensor, an eye dilation
sensor, a hydration sensor, a blood chemistry sensor, an ambient
temperature sensor, a humidity sensor, or a barometric pressure
sensor.
16. The method according to claim 1, wherein the individual is an
animal and the sensor derived data is generated from one or more
sensors comprising at least one of a blood pressure sensor, a heart
rate sensor, a body temperature sensor, an eye dilation sensor, a
hydration sensor, a blood chemistry sensor, an ambient temperature
sensor, a humidity sensor, or a barometric pressure sensor.
17. The method according to claim 1, wherein the individual is a
machine and the sensor derived data is generated from one or more
sensor comprising at least one a throttle position sensor, brake
position sensor, steering position sensor, axle, wheel or drive
shaft sensor, rotary position sensor, crankshaft position sensor,
engine coolant temperature sensor, water temperature sensor, oil
temperature sensor, fuel gauge sensor, oil gauge sensor, suspension
travel sensor, accelerometer, or pressure sensor.
18. The method according to claim 1, wherein the individual
dynamics/kinetics models comprise location history data for the
individual.
19. The method according to claim 1, wherein the at least one of
health models, fitness models, operation level models, or
performance level models comprise a respective one of a health
history data, a fitness history data, an operation level history
data, or a performance level history data.
20. The method according to claim 2 further comprising: comparing
the sensor derived data to pre-defined thresholds, and determining
whether the sensor-derived data satisfies the pre-defined
thresholds.
21. The method according to claim 1, wherein the individual is a
human being and the tag location data is indicative of a position
of at least one of a head, shoulder, a torso, an elbow, a hand, a
hip, a knee, and a foot.
22. The method according to claim 1, wherein the tag location data
comprises tag location data for each of two or more tags, each
indicative of a position for a different part of the
individual.
23. The method according to claim 22, wherein the tag location data
is indicative of foot position of each of two feet for the
individual, and wherein the method further comprises determining a
gait based on the tag location data.
24. The method according to claim 22, wherein the individual is a
human being and the tag location data is indicative of a position
of one or more of: each of two hands, each of two feet, each of two
knees, each of two hips, each of two elbows, and each of two
shoulders.
25. The method according to claim 1, wherein the sensor derived
data comprises a representation of sound made by the
individual.
26. The method according to claim 1, wherein comparing the tag
location data to individual dynamics/kinetics models comprises
selecting a dynamics/kinetics model for comparing, and wherein the
selecting the dynamics/kinetics model is based on identity
information.
27. The method according to claim 1, wherein comparing the tag
location data to individual dynamics/kinetics models comprises
selecting a dynamics/kinetics model for comparing, and wherein the
selecting the dynamics/kinetics model is based on zone data.
28. The method according to claim 1, wherein comparing the tag
location data to individual dynamics/kinetics models comprises
selecting a dynamics/kinetics model for comparing, and wherein the
selecting the dynamics/kinetics model is based on role data.
29. The method according to claim 1, wherein comparing the tag
location data to individual dynamics/kinetics models comprises
selecting a dynamics/kinetics model for comparing, and wherein the
selecting the dynamics/kinetics model is based on identity
information and role data.
30. A computer program product for monitoring an individual
comprising at least one computer-readable storage medium having
computer-executable program code instructions stored therein, the
computer-executable program code instructions comprising program
code instructions for: receiving tag derived data comprising tag
location data and blink data, wherein the tag location data is
generated based at least in part based on the blink data; receiving
sensor derived data indicative of at least one of a health, a
fitness, an operation level, or a performance level for the
individual; comparing the tag location data to individual
dynamics/kinetics models; comparing the sensor derived data to at
least one of health models, fitness models, operation level models,
or performance level models; and determining a health, fitness,
operation and performance (HFOP) status based on the comparing the
tag location data to individual dynamics/kinetics models and on the
comparing the sensor derived data to at least one of health models,
fitness models, operation level models, or performance level
models.
31. The computer program product according to claim 30, wherein the
computer-executable program code portions further comprise program
code instructions for: determining whether the HFOP status
satisfies a threshold value and generating, in an instance in which
the HFOP status does satisfy the threshold value, an alert.
32. The computer program product according to claim 30, wherein the
tag derived data is generated by a receiver configured to receive
tag signals generated by an ultra-wideband (UWB) transmitter.
33. The computer program product according to claim 30, wherein the
sensor derived data comprises environmental measurements generated
by one or more sensors comprising at least one of an accelerometer,
a diagnostic device, a triangulation positioner, or a proximity
positioner.
34. The computer program product according to claim 30, wherein the
sensor derived data comprises environmental measurements generated
by one or more sensors comprising at least one of a blood pressure
sensor, a heart rate sensor, a body temperature sensor, an eye
dilation sensor, a hydration sensor, a blood chemistry sensor, an
ambient temperature sensor, a humidity sensor, or a barometric
pressure sensor.
35. The computer program product according to claim 30, wherein the
sensor derived data comprises environmental measurements generated
by one or more sensors comprising at least one a throttle position
sensor, brake position sensor, steering position sensor, axle,
wheel or drive shaft sensor, rotary position sensor, crankshaft
position sensor, engine coolant temperature sensor, water
temperature sensor, oil temperature sensor, fuel gauge sensor, oil
gauge sensor, suspension travel sensor, accelerometer, or pressure
sensor.
36. The computer program product according to claim 30, wherein the
individual dynamics/kinetics models comprise location history data
for the individual.
37. The computer program product according to claim 30, wherein the
at least one of health models, fitness models, operation level
models, or performance level models comprise a respective one of a
health history data, a fitness history data, an operation level
history data, or a performance level history data.
38. The computer program product according to claim 30, wherein the
computer-executable program code portions further comprise program
code instructions for comparing the sensor derived data to
pre-defined thresholds, and generating an alert in an instance in
which particular sensor derived data satisfies an associated
threshold.
39. The computer program product according to claim 30, wherein the
tag location data comprises location data for each of two or more
tags, each indicative of a position for a different part of the
individual.
40. The computer program product according to claim 30, wherein the
tag-derived data is indicative of a human being and the tag
location data is indicative of a position of one or more of: each
of two hands, each of two feet, each of two knees, each of two
hips, each of two elbows, and each of two shoulders.
41. An apparatus comprising a processor and a memory having
computer code stored therein, the computer code configured, when
executed by the processor, to cause the apparatus to: receive tag
derived data comprising tag location data and blink data, wherein
the tag location data is determined based at least in part on the
blink data; receive sensor derived data indicative of at least one
of a health, a fitness, an operation level, or a performance level
for an individual; compare the tag location data to individual
dynamics/kinetics models; compare the sensor derived data to at
least one of health models, fitness models, operation level models,
or performance level models; and determine a health, fitness,
operation and performance (HFOP) status for the individual based on
the comparing the tag location data to individual dynamics/kinetics
models and on the comparing the sensor derived data to at least one
of health models, fitness models, operation level models, or
performance level models.
42. The apparatus of claim 41, wherein the received tag derived
data further comprises a tag UID and a tag-individual
correlator.
43. The apparatus of claim 41, wherein the received tag derived
data further comprises tag data, a tag UID, a tag-sensor
correlator, and a sensor information packet.
44. The apparatus of claim 41, wherein the sensor derived data
comprises a sensor UID and additional stored sensor data.
45. The apparatus of claim 41, wherein the sensor derived data
comprises a sensor UID, additional stored sensor data, a
sensor-individual correlator, and environmental measurements.
46. The apparatus of claim 41, wherein the sensor derived data
comprises a position calculation determined by a triangulation
positioner.
47. The apparatus of claim 41, wherein the sensor derived data
comprises position information read from a proximity label.
48. The apparatus of claim 41, wherein the sensor derived data
comprises a position calculation generated by a smartphone
comprising a triangulation positioner.
49. The apparatus of claim 41, wherein the sensor derived data
comprises a position generated by a smartphone comprising a barcode
imager.
50. The apparatus of claim 41, wherein the sensor derived data
comprises a position calculation determined by a triangulation
positioner, and wherein the sensor-derived data is determined, at
least in part, based on DGPS correction data.
51. A method for assessing a health, fitness, operation, or
performance of an individual, the method comprising: receiving tag
derived data comprising tag location data and blink data; comparing
the tag location data to one or more individual dynamics/kinetics
models; and determining a health, fitness, operation and
performance (HFOP) status for the individual based on the comparing
the tag location data to one or more individual dynamics/kinetics
models.
52. The method according to claim 51, wherein the tag location data
is determined based at least in part on the blink data.
53. The method according to claim 51, wherein the tag derived data
further comprises a tag-individual correlator.
54. The method according to claim 51, wherein the tag location data
comprises tag location data for each of two or more location tags,
each indicative of a position for a different part of the
individual.
55. The method according to claim 51, further comprising:
determining whether the HFOP status satisfies a threshold value and
generating, in an instance in which the HFOP status does satisfy
the threshold value, an alert.
56. The method according to claim 51, wherein the one or more
individual dynamics/kinetics models with which the tag location
data is compared is associated with at least one of role
information associated with the individual.
57. The method according to claim 51, wherein the one or more
individual dynamics/kinetics models with which the tag location
data is compared is associated with at least an individual identity
associated the individual.
58. The method according to claim 51, wherein the one or more
individual dynamics/kinetics models with which the tag location
data is compared is associated with at least zone data associated
with the individual.
59. The method according to claim 51, wherein the individual
dynamics/kinetics models comprise location history data for the
individual.
60. A method for assessing the health, fitness, operation, or
performance of an individual, the method comprising: receiving
sensor derived data indicative of at least one of a health, a
fitness, an operation level, or a performance level for the
individual; comparing the sensor derived data to a health, fitness,
operation and performance (HFOP) model; and determining a HFOP
status for the individual based on the comparing the sensor derived
data to at least one of health models, fitness models, operation
level models, or performance level models.
61. A method according to claim 60, further comprising: receiving
tag derived data comprising tag location data and blink data; and
comparing the tag location data to individual dynamics/kinetics
models; wherein the determination of the HFOP status for the
individual is further based on the comparing the tag location data
to one or more individual dynamics/kinetics models.
62. A method according to claim 60, wherein the at least one of
health models, fitness models, operation level models, or
performance level models comprise a respective one of a health
history data, a fitness history data, an operation level history
data, or a performance level history data.
63. The method according to claim 60 further comprising: comparing
the sensor derived data to pre-defined thresholds, and generating
an alert in an instance in which particular sensor derived data
satisfies an associated threshold.
64. The method according to claim 60, wherein the one or more
health models, fitness models, operation level models, or
performance level models with which the sensor derived data is
compared is associated with at least one of role information
associated with the individual, an individual identity associated
the individual, or zone data associated with the individual.
65. A computer program product for assessing a health, fitness,
operation, or performance of an individual, the computer program
product comprising at least one computer-readable storage medium
having computer-executable program code instructions stored
therein, the computer-executable program code instructions
comprising program code instructions for: receiving tag derived
data comprising tag location data and blink data; comparing the tag
location data to one or more individual dynamics/kinetics models;
and determining a health, fitness, operation and performance (HFOP)
status for the individual based on the comparing the tag location
data to one or more individual dynamics/kinetics models.
66. The computer program product according to claim 65, wherein the
tag location data is determined based at least in part on the blink
data.
67. The computer program product according to claim 65, wherein the
tag derived data further comprises a tag-individual correlator.
68. The computer program product according to claim 65, wherein the
tag location data comprises tag location data for each of two or
more location tags, each indicative of a position for a different
part of the individual.
69. The computer program product according to claim 65, wherein the
computer-executable program code instructions further comprise
program code instructions for: determining whether the HFOP status
satisfies a threshold value; and generating, in an instance in
which the HFOP status does satisfy the threshold value, an
alert.
70. The computer program product according to claim 65, wherein the
one or more individual dynamics/kinetics models with which the tag
location data is compared is associated with at least role
information associated with the individual.
71. The computer program product according to claim 65, wherein the
one or more individual dynamics/kinetics models with which the tag
location data is compared is associated with at least an individual
identity associated with the individual.
72. The computer program product according to claim 65, wherein the
one or more individual dynamics/kinetics models with which the tag
location data is compared is associated with at least zone data
associated with the individual.
73. The computer program product according to claim 65, wherein the
individual dynamics/kinetics models comprise location history data
for the individual.
74. A computer program product for assessing a health, fitness,
operation, or performance of an individual, comprising at least one
computer-readable storage medium having computer-executable program
code instructions stored therein, the computer-executable program
code instructions comprising program code instructions for:
receiving sensor derived data indicative of at least one of a
health, a fitness, an operation level, or a performance level for
the individual; comparing the sensor derived data to at least one
of health models, fitness models, operation level models, and
performance level models; and determining a health, fitness,
operation and performance (HFOP) status for the individual based on
the comparing the sensor derived data to at least one of health
models, fitness models, operation level models, or performance
level models.
75. The computer program product according to claim 74, wherein the
at least one of health models, fitness models, operation level
models, or performance level models comprise a respective one of a
health history data, a fitness history data, an operation level
history data, or a performance level history data.
76. The computer program product according to claim 74, wherein the
computer-executable program code portions further comprise program
code instructions for: comparing the sensor derived data to
pre-defined thresholds, and generating an alert in an instance in
which particular sensor derived data satisfies an associated
threshold.
77. The computer program product according to claim 74, wherein the
one or more health models, fitness models, operation level models,
or performance level models with which the tag location data is
compared is associated with at least one of role information
associated with the individual, an individual identity associated
the individual, or zone data associated with the individual.
78. An apparatus for assessing a health, fitness, operation, or
performance of an individual, the apparatus comprising a processor
and a memory having computer code stored therein, the computer code
configured, when executed by the processor, to cause the apparatus
to: receive tag derived data comprising tag location data and blink
data; compare the tag location data to one or more individual
dynamics/kinetics models; and determine a health, fitness,
operation and performance (HFOP) status for the individual based on
the comparison of the tag location data to one or more individual
dynamics/kinetics models.
79. The apparatus according to claim 78, wherein the tag location
data is determined based at least in part on the blink data.
80. The apparatus according to claim 78, wherein the tag derived
data further comprises a tag-individual correlator.
81. The apparatus according to claim 78, wherein the tag location
data comprises tag location data for each of two or more location
tags, each indicative of a position for a different part of the
individual.
82. The apparatus according to claim 78, wherein the computer code
is further configured, when executed by the processor, to cause the
apparatus to: determine whether the HFOP status satisfies a
threshold value and generating, in an instance in which the HFOP
status does satisfy the threshold value, an alert.
83. The apparatus according to claim 78, wherein the one or more
individual dynamics/kinetics models with which the tag location
data is compared is associated with at least role information
associated with the individual.
84. The apparatus according to claim 78, wherein the one or more
individual dynamics/kinetics models with which the tag location
data is compared is associated with at least an individual identity
associated with the individual.
85. The apparatus according to claim 78, wherein the one or more
individual dynamics/kinetics models with which the tag location
data is compared is associated with at least zone data associated
with the individual.
86. The apparatus according to claim 78, wherein the individual
dynamics/kinetics models comprise location history data for the
individual.
87. An apparatus for assessing the health, fitness, operation, or
performance of an individual, the apparatus comprising a processor
and a memory having computer code stored therein, the computer code
configured, when executed by the processor, to cause the apparatus
to: receive sensor derived data indicative of at least one of a
health, a fitness, an operation level, or a performance level for
the individual; compare the sensor derived data to at least one of
health models, fitness models, operation level models and
performance level models; and determine a health, fitness,
operation and performance (HFOP) status for the individual based on
the comparing the sensor derived data to at least one of health
models, fitness models, operation level models, or performance
level models.
88. The apparatus according to claim 87, wherein the at least one
of health models, fitness models, operation level models, or
performance level models comprise a respective one of a health
history data, a fitness history data, an operation level history
data, or a performance level history data.
89. The apparatus according to claim 87, wherein the computer code
is further configured, when executed by the processor, to cause the
apparatus to: compare the sensor derived data to pre-defined
thresholds, and generate an alert in an instance in which
particular sensor derived data satisfies an associated
threshold.
90. The apparatus according to claim 87, wherein the one or more
health models, fitness models, operation level models, or
performance level models with which the sensor derived data is
compared is associated with at least one of role information
associated with the individual, an individual identity associated
the individual, or zone data associated with the individual.
91. A method for monitoring an individual associated with a radio
frequency (RF) location tag, the method comprising: receiving tag
derived data comprising tag location data and blink data, wherein
the tag location data is determined based at least in part on the
blink data; receiving sensor derived data indicative of at least
one of a health, a fitness, an operation level, or a performance
level for the individual; receiving a tag-individual correlator
associated with the individual; receiving a tag-sensor correlator
associated with the RF location tag; and matching the tag-sensor
correlator with the tag-individual correlator to associate the
sensor-derived data with the individual.
92. The method according to claim 91, wherein the tag-individual
correlator is received from an individual database.
93. The method according to claim 91, wherein the tag-individual
correlator is determined from the blink data.
94. The method according to claim 91, wherein the tag-sensor
correlator is determined from the blink data.
95. A method for associating an environmental measurement with an
individual, the method comprising: associating an individual
location with the individual; receiving, from a sensor, a sensor
signal comprising one or more environmental measurements indicative
of at least one of a health, a fitness, an operation level, or a
performance level for the individual; associating a sensor location
with the sensor; and determining a sensor-individual correlator
based on the individual location being within a threshold distance
of the sensor location.
96. The method according to claim 95, wherein associating the
individual location with the individual comprises: receiving a tag
signal comprising blink data from a radio frequency (RF) location
tag associated with the individual; and determining the individual
location associated with the individual based at least in part on
the blink data.
97. The method according to claim 95, wherein associating the
individual location with the individual comprises: receiving, from
a second sensor associated with the individual, a second sensor
signal comprising a position calculation, the position calculation
determined by a triangulation positioner; and determining the
individual location associated with the individual based at least
in part on the position calculation.
98. The method according to claim 95, wherein associating the
individual location with the individual comprises: receiving, from
a second sensor associated with the individual, a second sensor
signal comprising position information, the position information
accessed from a proximity label; and determining the individual
location associated with the individual based at least in part on
the position information.
99. The method according to claim 95, wherein associating the
sensor location with the sensor comprises: receiving a tag signal
comprising blink data from a radio frequency (RF) location tag
associated with the sensor; and determining the sensor location
associated with the sensor based at least in part on the blink
data.
100. The method according to claim 95, wherein associating the
sensor location with the sensor comprises: receiving a tag signal
comprising blink data from a radio frequency (RF) reference tag
associated with the sensor, wherein said blink data includes a tag
unique identification number; determining a location of the RF
reference tag from a Geographical Information System based at
least, in part, on the tag unique identification number; and
determining the sensor location associated with the sensor based on
the location of the RF reference tag.
101. The method according to claim 95, wherein the sensor signal
received from the sensor further comprises a position calculation
determined by a triangulation positioner, wherein the sensor
location associated with the sensor is determined at least in part
on the position calculation.
102. The method according to claim 95, wherein the sensor signal
received from the sensor further comprises position information
read from a proximity label, wherein the sensor location associated
with the sensor is determined at least in part on the position
information.
103. The method according to claim 97, further comprising:
receiving a differential global positioning system (DGPS)
correction signal; and determining the individual location
associated with the individual based on the DGPS correction
signal.
104. The method according to claim 101 further comprising:
receiving a differential global positioning system (DGPS)
correction signal; and determining the sensor location based on the
DGPS correction signal.
105. The method according to claim 102 further comprising:
receiving a differential global positioning system (DGPS)
correction signal; and determining the sensor location based on the
DGPS correction signal.
106. A method for associating an environmental measurement with an
individual, the method comprising: associating an individual
location with the individual; receiving, from a sensor, a sensor
signal comprising one or more environmental measurements indicative
of at least one of a health, a fitness, an operation level, or a
performance level for the individual; associating a sensor location
with the sensor; and associating the sensor with the individual
based on the individual location being within a threshold distance
of the sensor location.
107. The method according to claim 106, further comprising:
associating a sensor information packet, derived from the sensor
signal, with the individual.
108. A computer program product for monitoring an individual
associated with a radio frequency (RF) location tag, comprising at
least one computer-readable storage medium having
computer-executable program code instructions stored therein, the
computer-executable program code instructions comprising program
code instructions for: receiving tag derived data comprising tag
location data and blink data, wherein the tag location data is
determined based at least in part on the blink data; receiving
sensor derived data indicative of at least one of a health, a
fitness, an operation level, or a performance level for the
individual; receiving a tag-individual correlator associated with
the individual; receiving a tag-sensor correlator associated with
the RF location tag; and matching the tag-sensor correlator with
the tag-individual correlator to associate the sensor-derived data
with the individual.
109. The computer program product according to claim 108, wherein
the tag-individual correlator is received from an individual
database.
110. The computer program product according to claim 108, wherein
the tag-individual correlator is determined from the blink
data.
111. The computer program product according to claim 108, wherein
the tag-sensor correlator is determined from the blink data.
112. A computer program product for associating an environmental
measurement with an individual comprising at least one
computer-readable storage medium having computer-executable program
code instructions stored therein, the computer-executable program
code instructions comprising program code instructions for:
associating an individual location with the individual; receiving,
from a sensor, a sensor signal comprising one or more environmental
measurements indicative of at least one of a health, a fitness, an
operation level, or a performance level for the individual;
associating a sensor location with the sensor; and determining a
sensor-individual correlator based on the individual location being
within a threshold distance of the sensor location.
113. The computer program product according to claim 112, wherein
associating the individual location with the individual comprises:
receiving a tag signal comprising blink data from a radio frequency
(RF) location tag associated with the individual; and determining
the individual location associated with the individual based at
least in part on the blink data.
114. The computer program product according to claim 112, wherein
associating the individual location with the individual comprises:
receiving, from a second sensor associated with the individual, a
second sensor signal comprising a position calculation, the
position calculation determined by a triangulation positioner; and
determining the individual location associated with the individual
based at least in part on the position calculation.
115. The computer program product according to claim 112, wherein
associating the individual location with the individual comprises:
receiving, from a second sensor associated with the individual, a
second sensor signal comprising position information, the position
information accessed from a proximity label; and determining the
individual location associated with the individual based at least
in part on the position information.
116. The computer program product according to claim 112, wherein
associating the sensor location with the sensor comprises:
receiving a tag signal comprising blink data from a radio frequency
(RF) location tag associated with the sensor; and determining the
sensor location associated with the sensor based at least in part
on the blink data.
117. The computer program product according to claim 112, wherein
associating the sensor location with the sensor comprises:
receiving a tag signal comprising blink data from a radio frequency
(RF) reference tag associated with the sensor, wherein said blink
data includes a tag unique identification number; determining a
location of the RF reference tag from a Geographical Information
System based at least, in part, on the tag unique identification
number; and determining the sensor location associated with the
sensor based on the location of the RF reference tag.
118. The computer program product according to claim 112, wherein
the sensor signal received from the sensor further comprises a
position calculation determined by a triangulation positioner,
wherein the sensor location associated with the sensor is
determined at least in part on the position calculation.
119. The computer program product according to claim 112, wherein
the sensor signal received from the sensor further comprises
position information read from a proximity label, wherein the
sensor location associated with the sensor is determined at least
in part on the position information.
120. The computer program product according to claim 118 wherein
the computer-executable program code instructions further comprise
program code instructions for: receiving a differential global
positioning system (DGPS) correction signal; and determining the
individual location associated with the individual based on the
DGPS correction signal.
121. The computer program product according to claim 118, wherein
the computer-executable program code instructions further comprise
program code instructions for: receiving a differential global
positioning system (DGPS) correction signal; and determining the
sensor location based on the DGPS correction signal.
122. The computer program product according to claim 119, wherein
the computer-executable program code instructions further comprise
program code instructions for: receiving a differential global
positioning system (DGPS) correction signal; and determining the
sensor location based on the DGPS correction signal.
123. An apparatus for monitoring an individual associated with a
radio frequency (RF) location tag, the apparatus comprising a
processor and a memory having computer code stored therein, the
computer code configured, when executed by the processor, to cause
the apparatus to: receive tag derived data comprising tag location
data and blink data, wherein the tag location data is determined
based at least in part on the blink data; receive sensor derived
data indicative of at least one of a health, a fitness, an
operation level, or a performance level for the individual; receive
a tag-individual correlator associated with the individual; receive
a tag-sensor correlator associated with the RF location tag; and
match the tag-sensor correlator with the tag-individual correlator
to associate the sensor-derived data with the individual.
124. The apparatus according to claim 123, wherein the
tag-individual correlator is received from an individual
database.
125. The apparatus according to claim 123, wherein the
tag-individual correlator is determined from the blink data.
126. The apparatus according to claim 123, wherein the tag-sensor
correlator is determined from the blink data.
127. The apparatus according to claim 123, wherein the tag-sensor
correlator is determined from the sensor-derived data.
128. An apparatus for associating an environmental measurement with
an individual, the apparatus comprising a processor and a memory
having computer code stored therein, the computer code configured,
when executed by the processor, to cause the apparatus to:
associate an individual location with the individual; receive, from
a sensor, a sensor signal comprising one or more environmental
measurements indicative of at least one of a health, a fitness, an
operation level, or a performance level for the individual;
associate a sensor location with the sensor; and determine a
sensor-individual correlator based on the individual location being
within a threshold distance of the sensor location.
129. The apparatus according to claim 128, wherein associating the
individual location with the individual comprises: receiving a tag
signal comprising blink data from a radio frequency (RF) location
tag associated with the individual; and determining the individual
location associated with the individual based at least in part on
the blink data.
130. The apparatus according to claim 128, wherein associating the
individual location with the individual comprises: receiving, from
a second sensor associated with the individual, a second sensor
signal comprising a position calculation, the position calculation
determined by a triangulation positioner; and determining the
individual location associated with the individual based at least
in part on the position calculation.
131. The apparatus according to claim 128, wherein associating the
individual location with the individual comprises: receiving, from
a second sensor associated with the individual, a second sensor
signal comprising position information, the position information
accessed from a proximity label; and determining the individual
location associated with the individual based at least in part on
the position information.
132. The apparatus according to claim 128, wherein associating the
sensor location with the sensor comprises: receiving a tag signal
comprising blink data from a radio frequency (RF) location tag
associated with the sensor; and determining the sensor location
associated with the sensor based at least in part on the blink
data.
133. The apparatus according to claim 128, wherein associating the
sensor location with the sensor comprises: receiving a tag signal
comprising blink data from a radio frequency (RF) reference tag
associated with the sensor, wherein said blink data includes a tag
unique identification number; determining a location of the RF
reference tag from a Geographical Information System based at
least, in part, on the tag unique identification number; and
determining the sensor location associated with the sensor based on
the location of the RF reference tag.
134. The apparatus according to claim 128, wherein the sensor
signal received from the sensor further comprises a position
calculation determined by a triangulation positioner, wherein the
sensor location associated with the sensor is determined at least
in part on the position calculation.
135. The apparatus according to claim 128, wherein the sensor
signal received from the sensor further comprises position
information read from a proximity label, wherein the sensor
location associated with the sensor is determined at least in part
on the position information.
136. The apparatus according to claim 134, wherein the computer
code is further configured, when executed by the processor, to
cause the apparatus to: receive a differential global positioning
system (DGPS) correction signal; and determine the sensor location
based on the DGPS correction signal.
137. The apparatus according to claim 135, wherein the computer
code is further configured, when executed by the processor, to
cause the apparatus to: receive a differential global positioning
system (DGPS) correction signal; and determine the sensor location
based on the DGPS correction signal.
138. An apparatus for associating an environmental measurement with
an individual, the apparatus comprising a processor and a memory
having computer code stored therein, the computer code configured,
when executed by the processor, to cause the apparatus to:
associate an individual location with the individual; receive, from
a sensor, a sensor signal comprising one or more environmental
measurements indicative of at least one of a health, a fitness, an
operation level, or a performance level for the individual;
associate a sensor location with the sensor; and associate the
sensor with an individual based on the individual location being
within a threshold distance of the sensor location.
139. An apparatus for associating an environmental measurement with
an individual, the apparatus comprising a processor and a memory
having computer code stored therein, the computer code configured,
when executed by the processor, to cause the apparatus to:
associate an individual location with the individual; receive, from
a sensor, a sensor signal comprising a sensor information packet;
associate a sensor location with the sensor; and associate the
sensor information packet with the individual based on the
individual location being within a threshold distance of the sensor
location.
140. A method for assessing a health or fitness of an individual,
the method comprising: receiving tag derived data comprising tag
location data and blink data; selecting an individual
dynamics/kinetics model from an individual dynamics/kinetics models
database; comparing the tag location data to the individual
dynamics/kinetics model; and determining a health, fitness,
operation and performance (HFOP) status for the individual based on
the comparison of the tag location data to the individual
dynamics/kinetics model.
141. The method according to claim 140, wherein selecting the
individual dynamics/kinetics model is based on at least an
individual identity.
142. The method according to claim 140, wherein selecting the
individual dynamics/kinetics model is based on at least a zone
determined from the tag location data.
143. The method according to claim 140, wherein selecting the
individual dynamics/kinetics model is based on at least a role.
144. The method according to claim 140, wherein selecting the
individual dynamics/kinetics model is based on at least a role and
an individual identity.
145. The method according to claim 140, wherein comparing the tag
location data to the individual dynamics/kinetics model is based at
least partially on adversarial data.
146. The method according to claim 145, wherein the tag derived
data comprises first blink data received from a first location tag
associated with a first individual and second blink data received
from a second location tag associated with a second individual, and
wherein the adversarial data is based at least partially on
comparing the first blink data to the second blink data.
147. The method according to claim 140, wherein comparing the tag
location data to the individual dynamics/kinetics model comprises
matching one or more field values of the tag location data to one
or more corresponding field values of the individual
dynamics/kinetic model.
148. The method according to claim 140, wherein comparing the tag
location data to the individual dynamics/kinetics model comprises
determining that at least one field value of the tag location data
has exceeded a control limit defined by the individual
dynamics/kinetic model.
149. The method according to claim 140, wherein comparing the tag
location data to the individual dynamics/kinetics model comprises
determining that at least one field value of the tag location data
is trending.
150. The method according to claim 140, wherein comparing the tag
location data to the individual dynamics/kinetics model comprises
determining that the tag location data falls within a cluster
range.
151. The method according to claim 140, wherein comparing the tag
location data to the individual dynamics/kinetics model comprises
calculating the covariance of the tag location data with the
individual dynamics/kinetics model.
152. A method for assessing a health, fitness, operation, or
performance of an individual in a monitored area, the method
comprising: receiving tag derived data comprising tag location data
and blink data, wherein the tag location data is determined based
at least in part on the blink data; receiving sensor derived data
indicative of at least one of a health, a fitness, an operation
level, or a performance level for the individual; selecting a
health, fitness, operation and performance (HFOP) model based on at
least a zone associated with or determined from the tag location
data; comparing the sensor derived data to the HFOP model; and
determining a HFOP status for the individual based on the
comparison of the sensor derived data to at least one of health
models, fitness models, operation level models, or performance
level models.
153. The method according to claim 152, wherein selecting a HFOP
model is based on at least an individual identity.
154. The method according to claim 152, wherein selecting a HFOP
model is based on at least a role.
155. The method according to claim 152, wherein selecting a HFOP
model is based on at least a role and an individual identity.
156. A method for monitoring an individual, the method comprising:
receiving tag derived data for each of two or more tags, the tag
derived data comprising tag location data and blink data, wherein
the tag location data is determined based at least in part on the
blink data; determining, based on an individual role database, that
the two or more tags are associated with individuals in an
adversarial role; and determining adversarial data based on the tag
location data.
157. The method according to claim 156, further comprising:
selecting an individual dynamics/kinetics model from an individual
dynamics/kinetics models database based on the adversarial data;
comparing the tag location data of at least one of the tags to the
individual dynamics/kinetics model; and determining a health,
fitness, operation and performance (HFOP) status for the individual
associated with the at least one tag based on the comparison of the
tag location data to the individual dynamics/kinetics model.
158. The method according to claim 156, further comprising:
receiving sensor derived data indicative of at least one of a
health, a fitness, an operation level, or a performance level for
at least one individual associated with at least one of the tags;
selecting a health, fitness, operation and performance (HFOP) model
based on at least the adversarial data; comparing the sensor
derived data to the HFOP model; and determining a HFOP status for
the individual based on the comparison of the sensor derived data
to at least one of health models, fitness models, operation level
models, or performance level models.
159. The method according to claim 156, further comprising:
selecting an individual dynamics/kinetics model from an individual
dynamics/kinetics models database based on the adversarial data;
comparing the tag location data of at least one of the tags to the
individual dynamics/kinetics model; receiving sensor derived data
indicative of at least one of a health, a fitness, an operation
level, or a performance level for at least one individual
associated with at least one of the tags; selecting a health,
fitness, operation and performance (HFOP) model based on at least
the adversarial data; comparing the sensor derived data to the HFOP
model; and determining a HFOP status for the individual based on
the comparison of the sensor derived data to at least one of health
models, fitness models, operation level models, or performance
level models and based on the comparison of the tag location data
to the individual dynamics/kinetics model.
160. A computer program product comprising at least one
computer-readable storage medium having computer-executable program
code instructions stored therein, the computer-executable program
code instructions comprising program code instructions for:
receiving tag derived data comprising tag location data and blink
data; selecting an individual dynamics/kinetics model from an
individual dynamics/kinetics models database; comparing the tag
location data to the individual dynamics/kinetics model; and
determining a health, fitness, operation and performance (HFOP)
status for the individual based on the comparison of the tag
location data to the individual dynamics/kinetics model.
161. The computer program product according to claim 160, wherein
selecting the individual dynamics/kinetics model is based on at
least an individual identity.
162. The computer program product according to claim 160, wherein
selecting the individual dynamics/kinetics model is based on at
least a zone determined from the tag location data.
163. The computer program product according to claim 160, wherein
selecting the individual dynamics/kinetics model is based on at
least a role.
164. The computer program product according to claim 160, wherein
selecting the individual dynamics/kinetics model is based on at
least a role and an individual identity.
165. The computer program product according to claim 160, wherein
comparing the tag location data to the individual dynamics/kinetics
model is based at least partially on adversarial data.
166. The computer program product according to claim 165, wherein
the tag derived data comprises first blink data received from a
first location tag associated with a first individual and second
blink data received from a second location tag associated with a
second individual, and wherein the adversarial data is based at
least partially on comparing the first blink data to the second
blink data.
167. The computer program product according to claim 160, wherein
comparing the tag location data to the individual dynamics/kinetics
model comprises matching one or more field values of the tag
location data to one or more corresponding field values of the
individual dynamics/kinetic model.
168. The computer program product according to claim 160, wherein
comparing the tag location data to the individual dynamics/kinetics
model comprises determining that at least one field value of the
tag location data has exceeded a control limit defined by the
individual dynamics/kinetic model.
169. The computer program product according to claim 160, wherein
comparing the tag location data to the individual dynamics/kinetics
model comprises determining that at least one field value of the
tag location data is trending.
170. The computer program product according to claim 160, wherein
comparing the tag location data to the individual dynamics/kinetics
model comprises determining that the tag location data falls within
a cluster range.
171. The computer program product according to claim 160, wherein
comparing the tag location data to the individual dynamics/kinetics
model comprises calculating the covariance of the tag location data
with the individual dynamics/kinetics model.
172. A computer program product for assessing a health, fitness,
operation, or performance of an individual in a monitored area, the
computer program product comprising at least one computer-readable
storage medium having computer-executable program code instructions
stored therein, the computer-executable program code instructions
comprising program code instructions for: receiving tag derived
data comprising tag location data and blink data, wherein the tag
location data is determined based at least in part on the blink
data; receiving sensor derived data indicative of at least one of a
health, a fitness, an operation level, or a performance level for
the individual; selecting a health, fitness, operation and
performance (HFOP) model based on at least a zone associated with
or determined from the tag location data; comparing the sensor
derived data to the HFOP model; and determining a HFOP status for
the individual based on the comparison of the sensor derived data
to at least one of health models, fitness models, operation level
models, or performance level models.
173. The computer program product according to claim 172, wherein
selecting a HFOP model is based on at least an individual
identity.
174. The computer program product according to claim 172, wherein
selecting a HFOP model is based on at least a role.
175. The computer program product according to claim 172, wherein
selecting a HFOP model is based on at least a role and an
individual identity.
176. A computer program product for monitoring an individual, the
computer program product comprising at least one computer-readable
storage medium having computer-executable program code instructions
stored therein, the computer-executable program code instructions
comprising program code instructions for: receiving tag derived
data for each of two or more tags, the tag derived data comprising
tag location data and blink data, wherein the tag location data is
determined based at least in part on the blink data; determining,
based on an individual role database, that the two or more tags are
associated with individuals in an adversarial role; determining
adversarial data based on the tag location data.
177. The computer program product according to claim 176, wherein
the computer-executable program code instructions further comprise
program code instructions for: selecting an individual
dynamics/kinetics model from an individual dynamics/kinetics models
database based on the adversarial data; comparing the tag location
data of at least one of the tags to the individual
dynamics/kinetics model; and determining a health, fitness,
operation and performance (HFOP) status for the individual
associated with the at least one tag based on the comparison of the
tag location data to the individual dynamics/kinetics model.
178. The computer program product according to claim 176, wherein
the computer-executable program code instructions further comprise
program code instructions for: receiving sensor derived data
indicative of at least one of a health, a fitness, an operation
level, or a performance level for at least one individual
associated with at least one of the tags; selecting a health,
fitness, operation and performance (HFOP) model based on at least
the adversarial data; comparing the sensor derived data to the HFOP
model; and determining a HFOP status for the individual based on
the comparison of the sensor derived data to at least one of health
models, fitness models, operation level models, or performance
level models.
179. The computer program product according to claim 176, wherein
the computer-executable program code instructions further comprise
program code instructions for: selecting an individual
dynamics/kinetics model from an individual dynamics/kinetics models
database based on the adversarial data; comparing the tag location
data of at least one of the tags to the individual
dynamics/kinetics model; receiving sensor derived data indicative
of at least one of a health, a fitness, an operation level, or a
performance level for at least one individual associated with at
least one of the tags; selecting a health, fitness, operation and
performance (HFOP) model based on at least the adversarial data;
comparing the sensor derived data to the HFOP model; and
determining a HFOP status for the individual based on the
comparison of the sensor derived data to at least one of health
models, fitness models, operation level models, or performance
level models and based on the comparison of the tag location data
to the individual dynamics/kinetics model.
180. An apparatus for assessing a health or fitness of an
individual, the apparatus comprising a processor and a memory
having computer code stored therein, the computer code configured,
when executed by the processor, to cause the apparatus to: receive
tag derived data comprising tag location data and blink data;
select an individual dynamics/kinetics model from an individual
dynamics/kinetics models database; compare the tag location data to
the individual dynamics/kinetics model; and determine a health,
fitness, operation and performance (HFOP) status for the individual
based on the comparison of the tag location data to the individual
dynamics/kinetics model.
181. The apparatus according to claim 180, wherein selecting the
individual dynamics/kinetics model is based on at least an
individual identity.
182. The apparatus according to claim 180, wherein selecting the
individual dynamics/kinetics model is based on at least a zone
determined from the tag location data.
183. The apparatus according to claim 180, wherein selecting the
individual dynamics/kinetics model is based on at least a role.
184. The apparatus according to claim 180, wherein selecting the
individual dynamics/kinetics model is based on at least a role and
an individual identity.
185. The apparatus according to claim 180, wherein comparing the
tag location data to the individual dynamics/kinetics model is
based at least partially on adversarial data.
186. The apparatus according to claim 185, wherein the tag derived
data comprises first blink data received from a first location tag
associated with a first individual and second blink data received
from a second location tag associated with a second individual, and
wherein the adversarial data is based at least partially on
comparing the first blink data to the second blink data.
187. The apparatus according to claim 180, wherein comparing the
tag location data to the individual dynamics/kinetics model
comprises matching one or more field values of the tag location
data to one or more corresponding field values of the individual
dynamics/kinetic model.
188. The apparatus according to claim 180, wherein comparing the
tag location data to the individual dynamics/kinetics model
comprises determining that at least one field value of the tag
location data has exceeded a control limit defined by the
individual dynamics/kinetic model.
189. The apparatus according to claim 180, wherein comparing the
tag location data to the individual dynamics/kinetics model
comprises determining that at least one field value of the tag
location data is trending.
190. The apparatus according to claim 180, wherein comparing the
tag location data to the individual dynamics/kinetics model
comprises determining that the tag location data falls within a
cluster range.
191. The apparatus according to claim 180, wherein comparing the
tag location data to the individual dynamics/kinetics model
comprises calculating the covariance of the tag location data with
the individual dynamics/kinetics model.
192. An apparatus for assessing a health, fitness, operation, or
performance of an individual in a monitored area, the apparatus
comprising a processor and a memory having computer code stored
therein, the computer code configured, when executed by the
processor, to cause the apparatus to: receive tag derived data
comprising tag location data and blink data, wherein the tag
location data is determined based at least in part on the blink
data; receive sensor derived data indicative of at least one of a
health, a fitness, an operation level, or a performance level for
the individual; select a health, fitness, operation and performance
(HFOP) model based on at least a zone associated with or determined
from the tag location data; compare the sensor derived data to the
HFOP model; and determine a HFOP status for the individual based on
the comparison of the sensor derived data to at least one of health
models, fitness models, operation level models, or performance
level models.
193. The apparatus according to claim 192, wherein selecting a HFOP
model is based on at least an individual identity.
194. The apparatus according to claim 192, wherein selecting a HFOP
model is based on at least a role.
195. The apparatus according to claim 192, wherein selecting a HFOP
model is based on at least a role and an individual identity.
196. An apparatus for monitoring an individual, the apparatus
comprising a processor and a memory having computer code stored
therein, the computer code configured, when executed by the
processor, to cause the apparatus to: receive tag derived data for
each of two or more tags, the tag derived data comprising tag
location data and blink data, wherein the tag location data is
determined based at least in part on the blink data; determine,
based on an individual role database, that the two or more tags are
associated with individuals in an adversarial role; and determine
adversarial data based on the tag location data.
197. The apparatus according to claim 196, wherein the computer
code is further configured, when executed by the processor, to
cause the apparatus to: select an individual dynamics/kinetics
model from an individual dynamics/kinetics models database based on
the adversarial data; compare the tag location data of at least one
of the tags to the individual dynamics/kinetics model; and
determine a health, fitness, operation and performance (HFOP)
status for the individual associated with the at least one tag
based on the comparison of the tag location data to the individual
dynamics/kinetics model.
198. The apparatus according to claim 196, wherein the computer
code is further configured, when executed by the processor, to
cause the apparatus to: receive sensor derived data indicative of
at least one of a health, a fitness, an operation level, or a
performance level for at least one individual associated with at
least one of the tags; select a health, fitness, operation and
performance (HFOP) model based on at least the adversarial data;
compare the sensor derived data to the HFOP model; and determine a
HFOP status for the individual based on the comparison of the
sensor derived data to at least one of health models, fitness
models, operation level models, or performance level models.
199. The apparatus according to claim 196, wherein the computer
code is further configured, when executed by the processor, to
cause the apparatus to: select an individual dynamics/kinetics
model from an individual dynamics/kinetics models database based on
the adversarial data; compare the tag location data of at least one
of the tags to the individual dynamics/kinetics model; receive
sensor derived data indicative of at least one of a health, a
fitness, an operation level, or a performance level for at least
one individual associated with at least one of the tags; select a
health, fitness, operation and performance (HFOP) model based on at
least the adversarial data; compare the sensor derived data to the
HFOP model; and determine a HFOP status for the individual based on
the comparison of the sensor derived data to at least one of health
models, fitness models, operation level models, or performance
level models and based on the comparison of the tag location data
to the individual dynamics/kinetics model.
200. A system for monitoring an individual, the system comprising:
one or more tags; and apparatus comprising a processor and a memory
having computer code stored therein, the computer code configured,
when executed by the processor, to cause the apparatus to: receive
tag derived data comprising tag location data and blink data,
wherein the tag location data is determined based at least in part
on the blink data; receive sensor derived data indicative of at
least one of a health, a fitness, an operation level, or a
performance level for an individual; compare the tag location data
to individual dynamics/kinetics models; compare the sensor derived
data to at least one of health models, fitness models, operation
level models, or performance level models; and determine a health,
fitness, operation and performance (HFOP) status for the individual
based on the comparing the tag location data to individual
dynamics/kinetics models and on the comparing the sensor derived
data to at least one of health models, fitness models, operation
level models, or performance level models.
201. The system according to claim 200, further comprising: one or
more sensors.
Description
CROSS-REFERENCE TO RELATED APPLICATION
[0001] This application claims priority from and the benefit of the
filing date of U.S. Provisional Patent Application No. 61/831,990
filed Jun. 6, 2013, the contents of which is incorporated by
reference in its entirety herein.
FIELD
[0002] Embodiments of the invention relate, generally, to
monitoring the health, fitness, operation, and performance of
individuals using a radio frequency ("RF") location system.
BACKGROUND
[0003] Individuals (e.g., persons, patients, athletes, animals,
machines, etc.) may encounter circumstances whereby their health,
fitness, operation, or performance becomes limited or compromised.
Applicant has discovered problems associated with current methods
and systems for monitoring the health, fitness, operation, or
performance of individuals. Through applied effort, ingenuity, and
innovation, Applicant has solved the identified problems by
developing a solution that is embodied by the present invention,
which is described in detail below.
BRIEF SUMMARY
[0004] In general, embodiments of the present invention provided
herein include systems, methods and computer readable media for
monitoring and determining status information concerning the
health, fitness, operation, and performance of individuals using a
RF location system.
[0005] In one embodiment, a method for monitoring an individual is
provided. The method comprising receiving tag derived data
comprising tag location data and blink data, wherein the tag
location data is determined based at least in part on the blink
data, receiving sensor derived data indicative of at least one of a
health, a fitness, an operation level, or a performance level for
the individual, comparing the tag location data to individual
dynamics/kinetics models, comparing the sensor derived data to at
least one of health models, fitness models, operation level models,
or performance level models, and determining a health, fitness,
operation and performance (HFOP) status for the individual based on
the comparing the tag location data to individual dynamics/kinetics
models and on the comparing the sensor derived data to at least one
of health models, fitness models, operation level models, or
performance level models.
[0006] In one embodiment, the method may further comprise
determining whether the HFOP status satisfies a threshold value and
generating, in an instance in which the HFOP status does satisfy
the threshold value, an alert. In one embodiment, the blink data is
generated by one or more tags comprising an ultra-wideband (UWB)
transmitter. In one embodiment, the UWB transmitter is configured
to transmit a plurality of time of arrival (TOA) timing pulses. In
one embodiment, the UWB transmitter is configured to transmit a tag
data packet comprising 112 bits.
[0007] In one embodiment, the tag derived data further comprises a
tag UID and a tag-individual correlator. In one embodiment, the tag
derived data further comprises tag data, a tag UID, a tag-sensor
correlator, and a sensor information packet. In one embodiment, the
sensor derived data comprises a sensor UID and additional stored
sensor data. In one embodiment, the sensor derived data comprises a
sensor UID, additional stored sensor data, a sensor-individual
correlator, and environmental measurements.
[0008] In one embodiment, the sensor derived data comprises a
position calculation determined by a triangulation positioner. In
one embodiment, the sensor derived data comprises position
information read from a proximity label. In one embodiment, the
sensor derived data comprises associated sensor data generated by a
sensor receiver upon receiving sensor information packets from
first and second sensors. In one embodiment, at least part of the
sensor derived data was transmitted from a location tag as part of
a tag data packet.
[0009] In one embodiment, the sensor derived data comprises
environmental measurements generated by one or more sensors
comprising at least one of an accelerometer, a diagnostic device, a
triangulation positioner, or a proximity positioner. In one
embodiment, the individual is a human being and the sensor derived
data is generated from one or more sensors comprising at least one
of a blood pressure sensor, a heart rate sensor, a body temperature
sensor, an eye dilation sensor, a hydration sensor, a blood
chemistry sensor, an ambient temperature sensor, a humidity sensor,
or a barometric pressure sensor.
[0010] In one embodiment, individual is an animal and the sensor
derived data is generated from one or more sensors comprising at
least one of a blood pressure sensor, a heart rate sensor, a body
temperature sensor, an eye dilation sensor, a hydration sensor, a
blood chemistry sensor, an ambient temperature sensor, a humidity
sensor, or a barometric pressure sensor. In one embodiment, the
individual is a machine and the sensor derived data is generated
from one or more sensor comprising at least one a throttle position
sensor, brake position sensor, steering position sensor, axle,
wheel or drive shaft sensor, rotary position sensor, crankshaft
position sensor, engine coolant temperature sensor, water
temperature sensor, oil temperature sensor, fuel gauge sensor, oil
gauge sensor, suspension travel sensor, accelerometer, or pressure
sensor.
[0011] In one embodiment, the individual dynamics/kinetics models
comprise location history data for the individual. In one
embodiment, the at least one of health models, fitness models,
operation level models, or performance level models comprise a
respective one of a health history data, a fitness history data, an
operation level history data, or a performance level history data.
In one embodiment, the method may further comprise comparing the
sensor derived data to pre-defined thresholds, and determining
whether the sensor-derived data satisfies the pre-defined
thresholds.
[0012] In one embodiment, the individual is a human being and the
tag location data is indicative of a position of at least one of a
head, shoulder, a torso, an elbow, a hand, a hip, a knee, and a
foot. In one embodiment, the tag location data comprises tag
location data for each of two or more tags, each indicative of a
position for a different part of the individual. In one embodiment,
the tag location data is indicative of foot position of each of two
feet for the individual, and wherein the method further comprises
determining a gait based on the tag location data. In one
embodiment, the individual is a human being and the tag location
data is indicative of a position of one or more of each of two
hands, each of two feet, each of two knees, each of two hips, each
of two elbows, and each of two shoulders. In one embodiment, the
sensor derived data comprises a representation of sound made by the
individual. In one embodiment, comparing the tag location data to
individual dynamics/kinetics models comprises selecting a
dynamics/kinetics model for comparing, and wherein the selecting
the dynamics/kinetics model is based on identity information.
[0013] In one embodiment, comparing the tag location data to
individual dynamics/kinetics models comprises selecting a
dynamics/kinetics model for comparing, and wherein the selecting
the dynamics/kinetics model is based on zone data. In one
embodiment, comparing the tag location data to individual
dynamics/kinetics models comprises selecting a dynamics/kinetics
model for comparing, and wherein the selecting the
dynamics/kinetics model is based on role data. In one embodiment,
comparing the tag location data to individual dynamics/kinetics
models comprises selecting a dynamics/kinetics model for comparing,
and wherein the selecting the dynamics/kinetics model is based on
identity information and role data.
[0014] In another embodiment, a computer program product for
monitoring an individual may be provided. The computer program
product may comprise at least one computer-readable storage medium
having computer-executable program code instructions stored
therein, the computer-executable program code instructions
comprising program code instructions for receiving tag derived data
comprising tag location data and blink data, wherein the tag
location data is generated based at least in part based on the
blink data, receiving sensor derived data indicative of at least
one of a health, a fitness, an operation level, or a performance
level for the individual, comparing the tag location data to
individual dynamics/kinetics models, comparing the sensor derived
data to at least one of health models, fitness models, operation
level models, or performance level models, and determining a
health, fitness, operation and performance (HFOP) status based on
the comparing the tag location data to individual dynamics/kinetics
models and on the comparing the sensor derived data to at least one
of health models, fitness models, operation level models, or
performance level models.
[0015] In one embodiment, the computer-executable program code
portions further comprise program code instructions for determining
whether the HFOP status satisfies a threshold value and generating,
in an instance in which the HFOP status does satisfy the threshold
value, an alert. In one embodiment, the tag derived data is
generated by a receiver configured to receive tag signals generated
by an ultra-wideband (UWB) transmitter. In one embodiment, the
sensor derived data comprises environmental measurements generated
by one or more sensors comprising at least one of an accelerometer,
a diagnostic device, a triangulation positioner, or a proximity
positioner. In one embodiment, the sensor derived data comprises
environmental measurements generated by one or more sensors
comprising at least one of a blood pressure sensor, a heart rate
sensor, a body temperature sensor, an eye dilation sensor, a
hydration sensor, a blood chemistry sensor, an ambient temperature
sensor, a humidity sensor, or a barometric pressure sensor.
[0016] In one embodiment, the sensor derived data comprises
environmental measurements generated by one or more sensors
comprising at least one a throttle position sensor, brake position
sensor, steering position sensor, axle, wheel or drive shaft
sensor, rotary position sensor, crankshaft position sensor, engine
coolant temperature sensor, water temperature sensor, oil
temperature sensor, fuel gauge sensor, oil gauge sensor, suspension
travel sensor, accelerometer, or pressure sensor. In one
embodiment, the individual dynamics/kinetics models comprise
location history data for the individual. In one embodiment, the at
least one of health models, fitness models, operation level models,
or performance level models comprise a respective one of a health
history data, a fitness history data, an operation level history
data, or a performance level history data.
[0017] In one embodiment, the computer-executable program code
portions further comprise program code instructions for comparing
the sensor derived data to pre-defined thresholds, and generating
an alert in an instance in which particular sensor derived data
satisfies an associated threshold. In one embodiment, the tag
location data comprises location data for each of two or more tags,
each indicative of a position for a different part of the
individual. In one embodiment, the tag-derived data is indicative
of a human being and the tag location data is indicative of a
position of one or more of each of two hands, each of two feet,
each of two knees, each of two hips, each of two elbows, and each
of two shoulders.
[0018] In another embodiment, an apparatus may be provided
comprising a processor and a memory having computer code stored
therein, the computer code configured, when executed by the
processor, to cause the apparatus to receive tag derived data
comprising tag location data and blink data, wherein the tag
location data is determined based at least in part on the blink
data, receive sensor derived data indicative of at least one of a
health, a fitness, an operation level, or a performance level for
an individual, compare the tag location data to individual
dynamics/kinetics models, compare the sensor derived data to at
least one of health models, fitness models, operation level models,
or performance level models, and determine a health, fitness,
operation and performance (HFOP) status for the individual based on
the comparing the tag location data to individual dynamics/kinetics
models and on the comparing the sensor derived data to at least one
of health models, fitness models, operation level models, or
performance level models.
[0019] In one embodiment, the received tag derived data further
comprises a tag UID and a tag-individual correlator. In one
embodiment, the received tag derived data further comprises tag
data, a tag UID, a tag-sensor correlator, and a sensor information
packet. In one embodiment, the sensor derived data comprises a
sensor UID and additional stored sensor data. In one embodiment,
the sensor derived data comprises a sensor UID, additional stored
sensor data, a sensor-individual correlator, and environmental
measurements.
[0020] In one embodiment, the sensor derived data comprises a
position calculation determined by a triangulation positioner. In
one embodiment, the sensor derived data comprises position
information read from a proximity label. In one embodiment, the
sensor derived data comprises a position calculation generated by a
smartphone comprising a triangulation positioner. In one
embodiment, the sensor derived data comprises a position generated
by a smartphone comprising a barcode imager. In one embodiment, the
sensor derived data comprises a position calculation determined by
a triangulation positioner, and wherein the sensor-derived data is
determined, at least in part, based on DGPS correction data.
[0021] In one embodiment a method for assessing a health, fitness,
operation, or performance of an individual is provided, the method
comprising receiving tag derived data comprising tag location data
and blink data, comparing the tag location data to one or more
individual dynamics/kinetics models, and determining a health,
fitness, operation and performance (HFOP) status for the individual
based on the comparing the tag location data to one or more
individual dynamics/kinetics models.
[0022] In one embodiment, the tag location data is determined based
at least in part on the blink data. In one embodiment, the tag
derived data further comprises a tag-individual correlator. In one
embodiment, the tag location data comprises tag location data for
each of two or more location tags, each indicative of a position
for a different part of the individual. In one embodiment the
method may further comprise determining whether the HFOP status
satisfies a threshold value and generating, in an instance in which
the HFOP status does satisfy the threshold value, an alert.
[0023] In one embodiment, the one or more individual
dynamics/kinetics models with which the tag location data is
compared may be associated with at least one of role information
associated with the individual. In one embodiment, the one or more
individual dynamics/kinetics models with which the tag location
data is compared is associated with at least an individual identity
associated the individual. In one embodiment, the one or more
individual dynamics/kinetics models with which the tag location
data is compared is associated with at least zone data associated
with the individual. In one embodiment, the individual
dynamics/kinetics models comprise location history data for the
individual.
[0024] In another embodiment a method for assessing the health,
fitness, operation, or performance of an individual is provided,
the method comprising receiving sensor derived data indicative of
at least one of a health, a fitness, an operation level, or a
performance level for the individual, comparing the sensor derived
data to a health, fitness, operation and performance (HFOP) model,
and determining a HFOP status for the individual based on the
comparing the sensor derived data to at least one of health models,
fitness models, operation level models, or performance level
models.
[0025] In one embodiment, the method may further comprise receiving
tag derived data comprising tag location data and blink data, and
comparing the tag location data to individual dynamics/kinetics
models, wherein the determination of the HFOP status for the
individual is further based on the comparing the tag location data
to one or more individual dynamics/kinetics models. In one
embodiment, the at least one of health models, fitness models,
operation level models, or performance level models comprise a
respective one of a health history data, a fitness history data, an
operation level history data, or a performance level history data.
In one embodiment the method may further comprise comparing the
sensor derived data to pre-defined thresholds, and generating an
alert in an instance in which particular sensor derived data
satisfies an associated threshold. In one embodiment, the one or
more health models, fitness models, operation level models, or
performance level models with which the sensor derived data is
compared is associated with at least one of role information
associated with the individual, an individual identity associated
the individual, or zone data associated with the individual.
[0026] In another embodiment a computer program product for
assessing a health, fitness, operation, or performance of an
individual is provided, the computer program product comprising at
least one computer-readable storage medium having
computer-executable program code instructions stored therein, the
computer-executable program code instructions comprising program
code instructions for receiving tag derived data comprising tag
location data and blink data, comparing the tag location data to
one or more individual dynamics/kinetics models, and determining a
health, fitness, operation and performance (HFOP) status for the
individual based on the comparing the tag location data to one or
more individual dynamics/kinetics models.
[0027] In one embodiment, the tag location data is determined based
at least in part on the blink data. In one embodiment, the tag
derived data further comprises a tag-individual correlator. In one
embodiment, the tag location data comprises tag location data for
each of two or more location tags, each indicative of a position
for a different part of the individual. In one embodiment, the
computer-executable program code instructions further comprise
program code instructions for determining whether the HFOP status
satisfies a threshold value, and generating, in an instance in
which the HFOP status does satisfy the threshold value, an
alert.
[0028] In one embodiment, the one or more individual
dynamics/kinetics models with which the tag location data is
compared is associated with at least role information associated
with the individual. In one embodiment, the one or more individual
dynamics/kinetics models with which the tag location data is
compared is associated with at least an individual identity
associated with the individual. In one embodiment, the one or more
individual dynamics/kinetics models with which the tag location
data is compared is associated with at least zone data associated
with the individual. In one embodiment, the individual
dynamics/kinetics models comprise location history data for the
individual.
[0029] In another embodiment a computer program product for
assessing a health, fitness, operation, or performance of an
individual is provided, the computer program product may comprise
at least one computer-readable storage medium having
computer-executable program code instructions stored therein, the
computer-executable program code instructions comprising program
code instructions for receiving sensor derived data indicative of
at least one of a health, a fitness, an operation level, or a
performance level for the individual, comparing the sensor derived
data to at least one of health models, fitness models, operation
level models, and performance level models, and determining a
health, fitness, operation and performance (HFOP) status for the
individual based on the comparing the sensor derived data to at
least one of health models, fitness models, operation level models,
or performance level models.
[0030] In one embodiment, the at least one of health models,
fitness models, operation level models, or performance level models
comprise a respective one of a health history data, a fitness
history data, an operation level history data, or a performance
level history data. In one embodiment, the computer-executable
program code portions further comprise program code instructions
for comparing the sensor derived data to pre-defined thresholds,
and generating an alert in an instance in which particular sensor
derived data satisfies an associated threshold. In one embodiment,
the one or more health models, fitness models, operation level
models, or performance level models with which the tag location
data is compared is associated with at least one of role
information associated with the individual, an individual identity
associated the individual, or zone data associated with the
individual.
[0031] In another embodiment, an apparatus for assessing a health,
fitness, operation, or performance of an individual is provided,
the apparatus comprising a processor and a memory having computer
code stored therein, the computer code configured, when executed by
the processor, to cause the apparatus to receive tag derived data
comprising tag location data and blink data, compare the tag
location data to one or more individual dynamics/kinetics models,
and determine a health, fitness, operation and performance (HFOP)
status for the individual based on the comparison of the tag
location data to one or more individual dynamics/kinetics
models.
[0032] In one embodiment, the tag location data is determined based
at least in part on the blink data. In one embodiment, the tag
derived data further comprises a tag-individual correlator. In one
embodiment, the tag location data comprises tag location data for
each of two or more location tags, each indicative of a position
for a different part of the individual. In one embodiment, the
computer code is further configured, when executed by the
processor, to cause the apparatus to determine whether the HFOP
status satisfies a threshold value and generating, in an instance
in which the HFOP status does satisfy the threshold value, an
alert.
[0033] In one embodiment, the one or more individual
dynamics/kinetics models with which the tag location data is
compared is associated with at least role information associated
with the individual. In one embodiment, the one or more individual
dynamics/kinetics models with which the tag location data is
compared is associated with at least an individual identity
associated with the individual. In one embodiment, the one or more
individual dynamics/kinetics models with which the tag location
data is compared is associated with at least zone data associated
with the individual. In one embodiment, the individual
dynamics/kinetics models comprise location history data for the
individual.
[0034] In another embodiment an apparatus for assessing the health,
fitness, operation, or performance of an individual is provided,
the apparatus comprising a processor and a memory having computer
code stored therein, the computer code configured, when executed by
the processor, to cause the apparatus to receive sensor derived
data indicative of at least one of a health, a fitness, an
operation level, or a performance level for the individual, compare
the sensor derived data to at least one of health models, fitness
models, operation level models and performance level models, and
determine a health, fitness, operation and performance (HFOP)
status for the individual based on the comparing the sensor derived
data to at least one of health models, fitness models, operation
level models, or performance level models.
[0035] In one embodiment, the at least one of health models,
fitness models, operation level models, or performance level models
comprise a respective one of a health history data, a fitness
history data, an operation level history data, or a performance
level history data. In one embodiment, the computer code is further
configured, when executed by the processor, to cause the apparatus
to compare the sensor derived data to pre-defined thresholds, and
generate an alert in an instance in which particular sensor derived
data satisfies an associated threshold. In one embodiment, the one
or more health models, fitness models, operation level models, or
performance level models with which the sensor derived data is
compared is associated with at least one of role information
associated with the individual, an individual identity associated
the individual, or zone data associated with the individual.
[0036] In one embodiment a method for monitoring an individual
associated with a radio frequency (RF) location tag is provided,
the method may comprise receiving tag derived data comprising tag
location data and blink data, wherein the tag location data is
determined based at least in part on the blink data, receiving
sensor derived data indicative of at least one of a health, a
fitness, an operation level, or a performance level for the
individual, receiving a tag-individual correlator associated with
the individual, receiving a tag-sensor correlator associated with
the RF location tag, and matching the tag-sensor correlator with
the tag-individual correlator to associate the sensor-derived data
with the individual. In one embodiment, the tag-individual
correlator is received from an individual database. In one
embodiment, the tag-individual correlator is determined from the
blink data. In one embodiment, the tag-sensor correlator is
determined from the blink data.
[0037] In another embodiment a method for associating an
environmental measurement with an individual is provided, the
method comprising associating an individual location with the
individual, receiving, from a sensor, a sensor signal comprising
one or more environmental measurements indicative of at least one
of a health, a fitness, an operation level, or a performance level
for the individual, associating a sensor location with the sensor,
and determining a sensor-individual correlator based on the
individual location being within a threshold distance of the sensor
location.
[0038] In one embodiment, associating the individual location with
the individual comprises receiving a tag signal comprising blink
data from a radio frequency (RF) location tag associated with the
individual, and determining the individual location associated with
the individual based at least in part on the blink data. In one
embodiment, associating the individual location with the individual
comprises receiving, from a second sensor associated with the
individual, a second sensor signal comprising a position
calculation, the position calculation determined by a triangulation
positioner, and determining the individual location associated with
the individual based at least in part on the position
calculation.
[0039] In one embodiment, associating the individual location with
the individual comprises receiving, from a second sensor associated
with the individual, a second sensor signal comprising position
information, the position information accessed from a proximity
label, and determining the individual location associated with the
individual based at least in part on the position information. In
one embodiment, associating the sensor location with the sensor
comprises receiving a tag signal comprising blink data from a radio
frequency (RF) location tag associated with the sensor, and
determining the sensor location associated with the sensor based at
least in part on the blink data.
[0040] In one embodiment, associating the sensor location with the
sensor comprises receiving a tag signal comprising blink data from
a radio frequency (RF) reference tag associated with the sensor,
wherein said blink data includes a tag unique identification
number, determining a location of the RF reference tag from a
Geographical Information System based at least, in part, on the tag
unique identification number, and determining the sensor location
associated with the sensor based on the location of the RF
reference tag. In another embodiment the method may further
comprise receiving a differential global positioning system (DGPS)
correction signal, and determining the sensor location based on the
DGPS correction signal.
[0041] In one embodiment, the sensor signal received from the
sensor further comprises a position calculation determined by a
triangulation positioner, wherein the sensor location associated
with the sensor is determined at least in part on the position
calculation. In one embodiment, the sensor signal received from the
sensor further comprises position information read from a proximity
label, wherein the sensor location associated with the sensor is
determined at least in part on the position information. In one
embodiment a method may further comprise receiving a differential
global positioning system (DGPS) correction signal, and determining
the individual location associated with the individual based on the
DGPS correction signal. In another embodiment the method may
further comprise receiving a differential global positioning system
(DGPS) correction signal, and determining the sensor location based
on the DGPS correction signal.
[0042] In another embodiment a method for associating an
environmental measurement with an individual is provided, the
method comprising associating an individual location with the
individual, receiving, from a sensor, a sensor signal comprising
one or more environmental measurements indicative of at least one
of a health, a fitness, an operation level, or a performance level
for the individual, associating a sensor location with the sensor,
and associating the sensor with the individual based on the
individual location being within a threshold distance of the sensor
location. In another embodiment the method may further comprise
associating a sensor information packet, derived from the sensor
signal, with the individual.
[0043] In another embodiment a computer program product for
monitoring an individual associated with a radio frequency (RF)
location tag may be provided, the computer program product
comprising at least one computer-readable storage medium having
computer-executable program code instructions stored therein, the
computer-executable program code instructions comprising program
code instructions for receiving tag derived data comprising tag
location data and blink data, wherein the tag location data is
determined based at least in part on the blink data, receiving
sensor derived data indicative of at least one of a health, a
fitness, an operation level, or a performance level for the
individual, receiving a tag-individual correlator associated with
the individual, receiving a tag-sensor correlator associated with
the RF location tag, and matching the tag-sensor correlator with
the tag-individual correlator to associate the sensor-derived data
with the individual. In one embodiment, the tag-individual
correlator is received from an individual database. In one
embodiment, the tag-individual correlator is determined from the
blink data. In one embodiment, the tag-sensor correlator is
determined from the blink data.
[0044] In another embodiment a computer program product for
associating an environmental measurement with an individual may be
provided, the computer program product comprising at least one
computer-readable storage medium having computer-executable program
code instructions stored therein, the computer-executable program
code instructions comprising program code instructions for
associating an individual location with the individual, receiving,
from a sensor, a sensor signal comprising one or more environmental
measurements indicative of at least one of a health, a fitness, an
operation level, or a performance level for the individual,
associating a sensor location with the sensor, and determining a
sensor-individual correlator based on the individual location being
within a threshold distance of the sensor location.
[0045] In one embodiment, associating the individual location with
the individual comprises receiving a tag signal comprising blink
data from a radio frequency (RF) location tag associated with the
individual, and determining the individual location associated with
the individual based at least in part on the blink data. In one
embodiment, associating the individual location with the individual
comprises receiving, from a second sensor associated with the
individual, a second sensor signal comprising a position
calculation, the position calculation determined by a triangulation
positioner, and determining the individual location associated with
the individual based at least in part on the position
calculation.
[0046] In one embodiment, associating the individual location with
the individual comprises receiving, from a second sensor associated
with the individual, a second sensor signal comprising position
information, the position information accessed from a proximity
label, and determining the individual location associated with the
individual based at least in part on the position information.
[0047] In one embodiment, associating the sensor location with the
sensor comprises receiving a tag signal comprising blink data from
a radio frequency (RF) location tag associated with the sensor, and
determining the sensor location associated with the sensor based at
least in part on the blink data.
[0048] In one embodiment, associating the sensor location with the
sensor comprises receiving a tag signal comprising blink data from
a radio frequency (RF) reference tag associated with the sensor,
wherein said blink data includes a tag unique identification
number, determining a location of the RF reference tag from a
Geographical Information System based at least, in part, on the tag
unique identification number, and determining the sensor location
associated with the sensor based on the location of the RF
reference tag. In one embodiment, the computer-executable program
code instructions further comprise program code instructions for
receiving a differential global positioning system (DGPS)
correction signal, and determining the individual location
associated with the individual based on the DGPS correction signal.
In one embodiment, the computer-executable program code
instructions further comprise program code instructions for
receiving a differential global positioning system (DGPS)
correction signal, and determining the sensor location based on the
DGPS correction signal.
[0049] In one embodiment, the sensor signal received from the
sensor further comprises a position calculation determined by a
triangulation positioner, wherein the sensor location associated
with the sensor is determined at least in part on the position
calculation. In one embodiment, the sensor signal received from the
sensor further comprises position information read from a proximity
label, wherein the sensor location associated with the sensor is
determined at least in part on the position information. In one
embodiment, the computer-executable program code instructions
further comprise program code instructions for receiving a
differential global positioning system (DGPS) correction signal,
and determining the sensor location based on the DGPS correction
signal.
[0050] In another embodiment an apparatus for monitoring an
individual associated with a radio frequency (RF) location tag may
be provided, the apparatus comprising a processor and a memory
having computer code stored therein, the computer code configured,
when executed by the processor, to cause the apparatus to receive
tag derived data comprising tag location data and blink data,
wherein the tag location data is determined based at least in part
on the blink data, receive sensor derived data indicative of at
least one of a health, a fitness, an operation level, or a
performance level for the individual, receive a tag-individual
correlator associated with the individual, receive a tag-sensor
correlator associated with the RF location tag, and match the
tag-sensor correlator with the tag-individual correlator to
associate the sensor-derived data with the individual. In one
embodiment, the tag-individual correlator is received from an
individual database. In one embodiment, the tag-individual
correlator is determined from the blink data. In one embodiment,
the tag-sensor correlator is determined from the blink data. In one
embodiment, the tag-sensor correlator is determined from the
sensor-derived data.
[0051] In another embodiment an apparatus for associating an
environmental measurement with an individual may be provided, the
apparatus comprising a processor and a memory having computer code
stored therein, the computer code configured, when executed by the
processor, to cause the apparatus to associate an individual
location with the individual, receive, from a sensor, a sensor
signal comprising one or more environmental measurements indicative
of at least one of a health, a fitness, an operation level, or a
performance level for the individual, associate a sensor location
with the sensor, and determine a sensor-individual correlator based
on the individual location being within a threshold distance of the
sensor location.
[0052] In one embodiment, associating the individual location with
the individual comprises receiving a tag signal comprising blink
data from a radio frequency (RF) location tag associated with the
individual, and determining the individual location associated with
the individual based at least in part on the blink data. In one
embodiment, associating the individual location with the individual
comprises receiving, from a second sensor associated with the
individual, a second sensor signal comprising a position
calculation, the position calculation determined by a triangulation
positioner, and determining the individual location associated with
the individual based at least in part on the position
calculation.
[0053] In one embodiment, associating the individual location with
the individual comprises receiving, from a second sensor associated
with the individual, a second sensor signal comprising position
information, the position information accessed from a proximity
label, and determining the individual location associated with the
individual based at least in part on the position information. In
one embodiment, associating the sensor location with the sensor
comprises receiving a tag signal comprising blink data from a radio
frequency (RF) location tag associated with the sensor, and
determining the sensor location associated with the sensor based at
least in part on the blink data.
[0054] In one embodiment, associating the sensor location with the
sensor comprises receiving a tag signal comprising blink data from
a radio frequency (RF) reference tag associated with the sensor,
wherein said blink data includes a tag unique identification
number, determining a location of the RF reference tag from a
Geographical Information System based at least, in part, on the tag
unique identification number, and determining the sensor location
associated with the sensor based on the location of the RF
reference tag.
[0055] In one embodiment, the sensor signal received from the
sensor further comprises a position calculation determined by a
triangulation positioner, wherein the sensor location associated
with the sensor is determined at least in part on the position
calculation. In one embodiment, the computer code is further
configured, when executed by the processor, to cause the apparatus
to receive a differential global positioning system (DGPS)
correction signal, and determine the sensor location based on the
DGPS correction signal.
[0056] In one embodiment, the sensor signal received from the
sensor further comprises position information read from a proximity
label, wherein the sensor location associated with the sensor is
determined at least in part on the position information. In one
embodiment, the computer code is further configured, when executed
by the processor, to cause the apparatus to receive a differential
global positioning system (DGPS) correction signal, and determine
the sensor location based on the DGPS correction signal.
[0057] In another embodiment an apparatus for associating an
environmental measurement with an individual may be provided, the
apparatus comprising a processor and a memory having computer code
stored therein, the computer code configured, when executed by the
processor, to cause the apparatus to associate an individual
location with the individual, receive, from a sensor, a sensor
signal comprising one or more environmental measurements indicative
of at least one of a health, a fitness, an operation level, or a
performance level for the individual, associate a sensor location
with the sensor, and associate the sensor with an individual based
on the individual location being within a threshold distance of the
sensor location.
[0058] In another embodiment an apparatus for associating an
environmental measurement with an individual may be provided, the
apparatus comprising a processor and a memory having computer code
stored therein, the computer code configured, when executed by the
processor, to cause the apparatus to associate an individual
location with the individual, receive, from a sensor, a sensor
signal comprising a sensor information packet, associate a sensor
location with the sensor, and associate the sensor information
packet with the individual based on the individual location being
within a threshold distance of the sensor location.
[0059] In one embodiment a method for assessing a health or fitness
of an individual is provided, the method comprising receiving tag
derived data comprising tag location data and blink data, selecting
an individual dynamics/kinetics model from an individual
dynamics/kinetics models database, comparing the tag location data
to the individual dynamics/kinetics model, and determining a
health, fitness, operation and performance (HFOP) status for the
individual based on the comparison of the tag location data to the
individual dynamics/kinetics model.
[0060] In one embodiment, selecting the individual
dynamics/kinetics model is based on at least an individual
identity. In one embodiment, selecting the individual
dynamics/kinetics model is based on at least a zone determined from
the tag location data. In one embodiment, selecting the individual
dynamics/kinetics model is based on at least a role. In one
embodiment, selecting the individual dynamics/kinetics model is
based on at least a role and an individual identity.
[0061] In one embodiment, comparing the tag location data to the
individual dynamics/kinetics model is based at least partially on
adversarial data. In one embodiment, the tag derived data comprises
first blink data received from a first location tag associated with
a first individual and second blink data received from a second
location tag associated with a second individual, and wherein the
adversarial data is based at least partially on comparing the first
blink data to the second blink data.
[0062] In one embodiment, comparing the tag location data to the
individual dynamics/kinetics model comprises matching one or more
field values of the tag location data to one or more corresponding
field values of the individual dynamics/kinetic model. In one
embodiment, comparing the tag location data to the individual
dynamics/kinetics model comprises determining that at least one
field value of the tag location data has exceeded a control limit
defined by the individual dynamics/kinetic model.
[0063] In one embodiment, comparing the tag location data to the
individual dynamics/kinetics model comprises determining that at
least one field value of the tag location data is trending. In one
embodiment, comparing the tag location data to the individual
dynamics/kinetics model comprises determining that the tag location
data falls within a cluster range. In one embodiment, comparing the
tag location data to the individual dynamics/kinetics model
comprises calculating the covariance of the tag location data with
the individual dynamics/kinetics model.
[0064] In another embodiment a method for assessing a health,
fitness, operation, or performance of an individual in a monitored
area may be provided, the method comprising receiving tag derived
data comprising tag location data and blink data, wherein the tag
location data is determined based at least in part on the blink
data, receiving sensor derived data indicative of at least one of a
health, a fitness, an operation level, or a performance level for
the individual, selecting a health, fitness, operation and
performance (HFOP) model based on at least a zone associated with
or determined from the tag location data, comparing the sensor
derived data to the HFOP model, and determining a HFOP status for
the individual based on the comparison of the sensor derived data
to at least one of health models, fitness models, operation level
models, or performance level models. In one embodiment, selecting a
HFOP model is based on at least an individual identity. In one
embodiment, selecting a HFOP model is based on at least a role. In
one embodiment, selecting a HFOP model is based on at least a role
and an individual identity.
[0065] In another embodiment a method for monitoring an individual
is provided, the method comprising receiving tag derived data for
each of two or more tags, the tag derived data comprising tag
location data and blink data, wherein the tag location data is
determined based at least in part on the blink data, determining,
based on an individual role database, that the two or more tags are
associated with individuals in an adversarial role, and determining
adversarial data based on the tag location data. In another
embodiment the method may further comprise selecting an individual
dynamics/kinetics model from an individual dynamics/kinetics models
database based on the adversarial data, comparing the tag location
data of at least one of the tags to the individual
dynamics/kinetics model, and determining a health, fitness,
operation and performance (HFOP) status for the individual
associated with the at least one tag based on the comparison of the
tag location data to the individual dynamics/kinetics model.
[0066] In another embodiment the method may further comprise
receiving sensor derived data indicative of at least one of a
health, a fitness, an operation level, or a performance level for
at least one individual associated with at least one of the tags,
selecting a health, fitness, operation and performance (HFOP) model
based on at least the adversarial data, comparing the sensor
derived data to the HFOP model, and determining a HFOP status for
the individual based on the comparison of the sensor derived data
to at least one of health models, fitness models, operation level
models, or performance level models.
[0067] In another embodiment the method may further comprise
selecting an individual dynamics/kinetics model from an individual
dynamics/kinetics models database based on the adversarial data,
comparing the tag location data of at least one of the tags to the
individual dynamics/kinetics model, receiving sensor derived data
indicative of at least one of a health, a fitness, an operation
level, or a performance level for at least one individual
associated with at least one of the tags, selecting a health,
fitness, operation and performance (HFOP) model based on at least
the adversarial data, comparing the sensor derived data to the HFOP
model, and determining a HFOP status for the individual based on
the comparison of the sensor derived data to at least one of health
models, fitness models, operation level models, or performance
level models and based on the comparison of the tag location data
to the individual dynamics/kinetics model.
[0068] In another embodiment a computer program product may be
provided, the computer program product comprising at least one
computer-readable storage medium having computer-executable program
code instructions stored therein, the computer-executable program
code instructions comprising program code instructions for
receiving tag derived data comprising tag location data and blink
data, selecting an individual dynamics/kinetics model from an
individual dynamics/kinetics models database, comparing the tag
location data to the individual dynamics/kinetics model, and
determining a health, fitness, operation and performance (HFOP)
status for the individual based on the comparison of the tag
location data to the individual dynamics/kinetics model.
[0069] In one embodiment, selecting the individual
dynamics/kinetics model is based on at least an individual
identity. In one embodiment, selecting the individual
dynamics/kinetics model is based on at least a zone determined from
the tag location data. In one embodiment, selecting the individual
dynamics/kinetics model is based on at least a role In one
embodiment, selecting the individual dynamics/kinetics model is
based on at least a role and an individual identity.
[0070] In one embodiment, comparing the tag location data to the
individual dynamics/kinetics model is based at least partially on
adversarial data. In one embodiment, the tag derived data comprises
first blink data received from a first location tag associated with
a first individual and second blink data received from a second
location tag associated with a second individual, and wherein the
adversarial data is based at least partially on comparing the first
blink data to the second blink data In one embodiment, comparing
the tag location data to the individual dynamics/kinetics model
comprises matching one or more field values of the tag location
data to one or more corresponding field values of the individual
dynamics/kinetic model.
[0071] In one embodiment, comparing the tag location data to the
individual dynamics/kinetics model comprises determining that at
least one field value of the tag location data has exceeded a
control limit defined by the individual dynamics/kinetic model. In
one embodiment, comparing the tag location data to the individual
dynamics/kinetics model comprises determining that at least one
field value of the tag location data is trending. In one
embodiment, comparing the tag location data to the individual
dynamics/kinetics model comprises determining that the tag location
data falls within a cluster range. In one embodiment, comparing the
tag location data to the individual dynamics/kinetics model
comprises calculating the covariance of the tag location data with
the individual dynamics/kinetics model.
[0072] In another embodiment, a computer program product for
assessing a health, fitness, operation, or performance of an
individual in a monitored area may be provided, the computer
program product comprising at least one computer-readable storage
medium having computer-executable program code instructions stored
therein, the computer-executable program code instructions
comprising program code instructions for receiving tag derived data
comprising tag location data and blink data, wherein the tag
location data is determined based at least in part on the blink
data,
[0073] receiving sensor derived data indicative of at least one of
a health, a fitness, an operation level, or a performance level for
the individual, selecting a health, fitness, operation and
performance (HFOP) model based on at least a zone associated with
or determined from the tag location data, comparing the sensor
derived data to the HFOP model, and determining a HFOP status for
the individual based on the comparison of the sensor derived data
to at least one of health models, fitness models, operation level
models, or performance level models. In one embodiment, selecting a
HFOP model is based on at least an individual identity. In one
embodiment, selecting a HFOP model is based on at least a role. In
one embodiment, selecting a HFOP model is based on at least a role
and an individual identity.
[0074] In one embodiment, a computer program product for monitoring
an individual may be provided, the computer program product
comprising at least one computer-readable storage medium having
computer-executable program code instructions stored therein, the
computer-executable program code instructions comprising program
code instructions for receiving tag derived data for each of two or
more tags, the tag derived data comprising tag location data and
blink data, wherein the tag location data is determined based at
least in part on the blink data, determining, based on an
individual role database, that the two or more tags are associated
with individuals in an adversarial role, determining adversarial
data based on the tag location data.
[0075] In one embodiment, the computer-executable program code
instructions further comprise program code instructions for
selecting an individual dynamics/kinetics model from an individual
dynamics/kinetics models database based on the adversarial data,
comparing the tag location data of at least one of the tags to the
individual dynamics/kinetics model, and determining a health,
fitness, operation and performance (HFOP) status for the individual
associated with the at least one tag based on the comparison of the
tag location data to the individual dynamics/kinetics model. In one
embodiment, the computer-executable program code instructions
further comprise program code instructions for receiving sensor
derived data indicative of at least one of a health, a fitness, an
operation level, or a performance level for at least one individual
associated with at least one of the tags, selecting a health,
fitness, operation and performance (HFOP) model based on at least
the adversarial data, comparing the sensor derived data to the HFOP
model, and determining a HFOP status for the individual based on
the comparison of the sensor derived data to at least one of health
models, fitness models, operation level models, or performance
level models.
[0076] In one embodiment, the computer-executable program code
instructions further comprise program code instructions for
selecting an individual dynamics/kinetics model from an individual
dynamics/kinetics models database based on the adversarial data,
comparing the tag location data of at least one of the tags to the
individual dynamics/kinetics model, receiving sensor derived data
indicative of at least one of a health, a fitness, an operation
level, or a performance level for at least one individual
associated with at least one of the tags, selecting a health,
fitness, operation and performance (HFOP) model based on at least
the adversarial data, comparing the sensor derived data to the HFOP
model, and determining a HFOP status for the individual based on
the comparison of the sensor derived data to at least one of health
models, fitness models, operation level models, or performance
level models and based on the comparison of the tag location data
to the individual dynamics/kinetics model.
[0077] In another embodiment an apparatus for assessing a health or
fitness of an individual is provided, the apparatus comprising a
processor and a memory having computer code stored therein, the
computer code configured, when executed by the processor, to cause
the apparatus to receive tag derived data comprising tag location
data and blink data, select an individual dynamics/kinetics model
from an individual dynamics/kinetics models database, compare the
tag location data to the individual dynamics/kinetics model, and
determine a health, fitness, operation and performance (HFOP)
status for the individual based on the comparison of the tag
location data to the individual dynamics/kinetics model.
[0078] In one embodiment, selecting the individual
dynamics/kinetics model is based on at least an individual
identity. In one embodiment, selecting the individual
dynamics/kinetics model is based on at least a zone determined from
the tag location data. In one embodiment, selecting the individual
dynamics/kinetics model is based on at least a role. In one
embodiment, selecting the individual dynamics/kinetics model is
based on at least a role and an individual identity. In one
embodiment, comparing the tag location data to the individual
dynamics/kinetics model is based at least partially on adversarial
data. In one embodiment, the tag derived data comprises first blink
data received from a first location tag associated with a first
individual and second blink data received from a second location
tag associated with a second individual, and wherein the
adversarial data is based at least partially on comparing the first
blink data to the second blink data.
[0079] In one embodiment, comparing the tag location data to the
individual dynamics/kinetics model comprises matching one or more
field values of the tag location data to one or more corresponding
field values of the individual dynamics/kinetic model. In one
embodiment, comparing the tag location data to the individual
dynamics/kinetics model comprises determining that at least one
field value of the tag location data has exceeded a control limit
defined by the individual dynamics/kinetic model. In one
embodiment, comparing the tag location data to the individual
dynamics/kinetics model comprises determining that at least one
field value of the tag location data is trending. In one
embodiment, comparing the tag location data to the individual
dynamics/kinetics model comprises determining that the tag location
data falls within a cluster range. In one embodiment, comparing the
tag location data to the individual dynamics/kinetics model
comprises calculating the covariance of the tag location data with
the individual dynamics/kinetics model.
[0080] In one embodiment, an apparatus for assessing a health,
fitness, operation, or performance of an individual in a monitored
area may be provided, the apparatus comprising a processor and a
memory having computer code stored therein, the computer code
configured, when executed by the processor, to cause the apparatus
to receive tag derived data comprising tag location data and blink
data, wherein the tag location data is determined based at least in
part on the blink data, receive sensor derived data indicative of
at least one of a health, a fitness, an operation level, or a
performance level for the individual, select a health, fitness,
operation and performance (HFOP) model based on at least a zone
associated with or determined from the tag location data, compare
the sensor derived data to the HFOP model, and determine a HFOP
status for the individual based on the comparison of the sensor
derived data to at least one of health models, fitness models,
operation level models, or performance level models. In one
embodiment, selecting a HFOP model is based on at least an
individual identity. In one embodiment, selecting a HFOP model is
based on at least a role. In one embodiment, selecting a HFOP model
is based on at least a role and an individual identity.
[0081] In another embodiment, an apparatus for monitoring an
individual may be provided, the apparatus comprising a processor
and a memory having computer code stored therein, the computer code
configured, when executed by the processor, to cause the apparatus
to receive tag derived data for each of two or more tags, the tag
derived data comprising tag location data and blink data, wherein
the tag location data is determined based at least in part on the
blink data, determine, based on an individual role database, that
the two or more tags are associated with individuals in an
adversarial role, and determine adversarial data based on the tag
location data. In one embodiment, the computer code is further
configured, when executed by the processor, to cause the apparatus
to select an individual dynamics/kinetics model from an individual
dynamics/kinetics models database based on the adversarial data,
compare the tag location data of at least one of the tags to the
individual dynamics/kinetics model, and determine a health,
fitness, operation and performance (HFOP) status for the individual
associated with the at least one tag based on the comparison of the
tag location data to the individual dynamics/kinetics model.
[0082] In one embodiment, the computer code is further configured,
when executed by the processor, to cause the apparatus to receive
sensor derived data indicative of at least one of a health, a
fitness, an operation level, or a performance level for at least
one individual associated with at least one of the tags, select a
health, fitness, operation and performance (HFOP) model based on at
least the adversarial data, compare the sensor derived data to the
HFOP model, and determine a HFOP status for the individual based on
the comparison of the sensor derived data to at least one of health
models, fitness models, operation level models, or performance
level models.
[0083] In one embodiment, the computer code is further configured,
when executed by the processor, to cause the apparatus to select an
individual dynamics/kinetics model from an individual
dynamics/kinetics models database based on the adversarial data,
compare the tag location data of at least one of the tags to the
individual dynamics/kinetics model, receive sensor derived data
indicative of at least one of a health, a fitness, an operation
level, or a performance level for at least one individual
associated with at least one of the tags, select a health, fitness,
operation and performance (HFOP) model based on at least the
adversarial data, compare the sensor derived data to the HFOP
model, and determine a HFOP status for the individual based on the
comparison of the sensor derived data to at least one of health
models, fitness models, operation level models, or performance
level models and based on the comparison of the tag location data
to the individual dynamics/kinetics model.
[0084] In some embodiments, a system may be provided. The system
may comprise at least one of the above recited apparatuses. In some
embodiments, the system may further comprise one or more tags. In
some embodiments, the system may further comprise one or more
sensors.
BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)
[0085] Having thus described embodiments of the invention in
general terms, reference will now be made to the accompanying
drawings, which are not necessarily drawn to scale, and
wherein:
[0086] FIG. 1 is block diagram of a RF location system that is
configured to monitor the health, fitness, operation, and
performance of individuals in accordance with an example
embodiment;
[0087] FIGS. 2A and 2B are example illustrations of individuals
equipped with an exemplary arrangement of tags and sensors in
accordance with some example embodiments;
[0088] FIGS. 3A-3E are block diagrams showing the input and output
of receivers in accordance with an example embodiment;
[0089] FIGS. 4A and 4B are block diagrams of receiver processing
and analytics systems that are configured to monitor the health,
fitness, operation, and performance of individuals in accordance
with example embodiments;
[0090] FIGS. 5A-5E are block diagrams showing contents of one or
more of an individual database and a role database in accordance
with an example embodiment;
[0091] FIGS. 6A-6C are block diagrams showing data associations
that may be used in accordance with an example embodiment;
[0092] FIG. 7 is a flowchart illustrating a method for monitoring
the health, fitness, operation, and performance of individuals in
accordance with an example embodiment;
[0093] FIG. 8 is a flowchart illustrating a method for use in a tag
data/sensor data filter in accordance with an example
embodiment;
[0094] FIG. 9 is a flowchart illustrating a method for use in a
health, fitness, operation and performance engine, in accordance
with an example embodiment;
[0095] FIG. 10 is a flowchart illustrating a method for use in an
individual dynamics/kinetics engine in accordance with an example
embodiment;
[0096] FIG. 11 is a flowchart illustrating a method for use in an
health, fitness, operation and performance (HFOP) status engine in
accordance with an example embodiment;
[0097] FIGS. 12A-12C are flowcharts showing methods of use in
associating sensor data to particular individuals in accordance
with an example embodiment;
[0098] FIG. 13 is an example illustration showing how the use of
tag data may be utilized to monitor the health, fitness, operation,
or performance of an individual, in accordance with an example
embodiment;
[0099] FIG. 14 is a flowchart showing an example embodiment of the
monitoring of the health, fitness, operation, or performance of an
individual, in accordance with an example embodiment; and
[0100] FIG. 15 is a block diagram of an apparatus that may be
specifically configured in accordance with an example embodiment of
the present invention.
DETAILED DESCRIPTION
[0101] Embodiments of the present invention now will be described
more fully hereinafter with reference to the accompanying drawings,
in which some, but not all embodiments of the inventions are shown.
Indeed, embodiments of the invention may be embodied in many
different forms and should not be construed as limited to the
embodiments set forth herein; rather, these embodiments are
provided so that this disclosure will satisfy applicable legal
requirements. Like numbers refer to like elements throughout.
[0102] As used herein, the terms "data," "content," "information"
and similar terms may be used interchangeably to refer to data
capable of being captured, transmitted, received, displayed and/or
stored in accordance with various example embodiments. Thus, use of
any such terms should not be taken to limit the spirit and scope of
the disclosure. Further, where a computing device is described
herein to receive data from another computing device, it will be
appreciated that the data may be received directly from the another
computing device or may be received indirectly via one or more
intermediary computing devices, such as, for example, one or more
servers, relays, routers, network access points, base stations,
and/or the like, sometimes referred to herein as a "network."
Similarly, where a computing device is described herein to send
data to another computing device, it will be appreciated that the
data may be sent directly to the another computing device or may be
sent indirectly via one or more intermediary computing devices,
such as, for example, one or more servers, relays, routers, network
access points, base stations, and/or the like.
BRIEF OVERVIEW
[0103] The methods, apparatus and computer program products
described herein are operable to monitor the health, fitness,
operation, and performance of individuals. In some embodiments, the
health, fitness, operation, and performance of individuals are
monitored based on highly accurate location and/or position data
associated with the monitored individuals. Such highly accurate
location and/or position data may be further improved with the use
of sensor data to provide real-time health, fitness, operation, and
performance information concerning the monitored individuals.
[0104] The term "individual" as used herein refers to a person,
patient, athlete, an animal, a machine (e.g., a race car), or other
entity having health, fitness, operation, or performance levels
that may be deemed appropriate for remote monitoring.
[0105] Health may generally refer to a condition (e.g., healthy,
sick, injured, tired, stressed, dehydrated, dizzy) of an
individual. Health may include, but is not limited to, identifying
whether an individual is healthy and if not, which of one or more
conditions the individual may be afflicted with (e.g., the flu). A
health model may include, but is not limited to, particular sensor
data and associated thresholds or ranges of health related
parameters that may indicate one or more conditions.
[0106] Fitness may generally refer to whether an individual or
object is capable or suitable for performing a function. Fitness
may include, but is not limited to, an ability to perform a
function. A fitness model may include, but is not limited to,
particular sensor data and associated thresholds or ranges of
fitness parameters that may indicate one or more functions. For
example, if sensor data indicates a stride length of 20+ feet
and/or a sustained heart rate of 220+ beats per minutes, an
associated fitness model may indicate a horse is performing such
feats, and may then look to functions horses may perform. Operation
may generally relate to the practical application of a process.
Operation may include, but is not limited to, identifying whether
an individual is injured or not, and if so, with what injury an
individual may be afflicted with (e.g., sprained ankle) and/or at
what level may the individual operate at. An operation model may
include, but is not limited to, particular sensor data and
associated thresholds or ranges that may indicate one or more
injuries and/or a percentage of optimal operation. As an example,
an individual athlete may take a mental test which can be used as a
baseline operation model. If the individual athlete then takes a
similar mental test after a head injury and performance differs
significantly from the first mental test, the individual may be
diagnosed as being in a dizzy or disoriented condition and may
predict that the injury cause of the condition is a concussion.
[0107] Performance may include, but is not limited to, the manner
or quality of functioning or an indication of a performance level.
In one embodiment, performance may be generally described as a
vector of measurements as discussed in greater detail below. A
performance model may include, but is not limited to, particular
sensor data and associated thresholds or ranges that may indicate
performance quality and/or one or more performance levels. In the
concussion example, the mental test defines the measurements for
the operation model. When the athlete takes the initial test, the
measurements are recorded, establishing the baseline performance
level. When the athlete takes the second test, the measurements are
recorded, establishing a second performance level. Comparing the
second performance level to the baseline performance level involves
comparing at least some of the measurements from the second test to
the corresponding measurements from the first test.
[0108] There are many reasons why one may wish to monitor the
health, fitness, operation, and performance of individuals in real
time or near-real time. For example, one may wish to make sure that
if a person is hurt that someone else is alerted to provide aid,
e.g., a parent, a trainer, a paramedic or other health care
professional. In some cases, it may be obvious that a person is
hurt (e.g., they fall down, cry, scream, or become unconscious). In
other circumstances, their injuries may be less obvious (e.g.,
stimulus response times may be slowed, they may limp, or they may
otherwise move or perform abnormally). In still other
circumstances, health, fitness, operation, and performance
degradations may only be determined using sensors making
appropriate measurements (e.g., heart rate, breathing, body
temperature, blood chemistry, etc.). In certain circumstances,
multiple measurements may be needed to identify degradations.
[0109] With the advent of location tracking in sports, it is
possible to use precise position information to facilitate
monitoring of health and fitness of players, referees, bat boys,
coaches, mascots, beer vendors, racing sausages, or even fans in
new ways that are easier, faster, and can provide additional
information. In this regard, dynamics/kinetics models may be
utilized also. A dynamics/kinetics model may include, but is not
limited to, position information and associated actions. For
example, location data indicating a track shape may indicate an
action of "running on a track" or the like. In another embodiment,
actions may include or be indicative of non-actions, such as lying
down or standing still. In another example, multi-dimensional
position information that includes arm/hand position and leg/foot
position could indicate that a person is running, lying on the
ground, or positioned in a three-point football stance.
[0110] Various embodiments of the invention are directed to
monitoring the health, fitness, operation, and performance of
individuals using a RF location system that is configured to
aggregate location information with other sensor data. In this
regard, such embodiments are configurable to provide alerts,
analytics, and statistics that may be used to diagnose, treat, and
improve the health, fitness, operation, and performance of
individuals.
Example RF Location System Architecture
[0111] FIG. 1 illustrates a radio frequency locating system useful
for determining the location of an object (e.g. a football player
on a football field) by determining RF location tag 102 (e.g., a
ultra-wide band (UWB) location tag) location information at each
receiver 106 (e.g., UWB reader, etc.); a timing reference clock to
synchronize the frequency of counters within each receiver 106;
and, in some examples, a reference tag 104, preferably a UWB
transmitter, positioned at known coordinates to enable phase offset
between counters to be determined. The systems described herein may
be referred to as either "multilateration" or "geolocation"
systems; terms which refer to the process of locating a signal
source by solving for the mathematical intersection of multiple
hyperbolae determined by the difference of arrival times of a
signal received at multiple receivers.
[0112] In some examples, the system comprising at least the tags
102 and the receivers 106 is configured to provide two dimensional
and/or three dimensional precision localization (e.g., subfoot
resolutions), even in the presence of multipath interference, due
in part to the use of short nanosecond duration pulses whose
time-of-flight can be accurately determined using detection
circuitry, such as in the receivers 106, which can trigger on the
leading edge of a received waveform. In some examples, this short
pulse characteristic allows necessary data to be conveyed by the
system at a higher peak power, but lower overall power levels, than
a wireless system configured for high data rate communications, yet
still operate within local regulatory requirements which may limit
overall power levels.
[0113] In some examples, the tags 102 may operate with an
instantaneous -3 dB bandwidth of approximately 400 MHz and an
average transmission rate below a 187.5 kHz regulatory cutoff. In
such examples, the predicted maximum range of the system, operating
at 6.0 GHz, is roughly 311 meters. Such a configuration
advantageously satisfies constraints applied by regulatory bodies
related to peak and average power densities (e.g., effective
isotropic radiated power density), while still optimizing system
performance related to range and interference. In further examples,
tag transmissions with a -3 dB bandwidth of approximately 400 MHz
yields, in some examples, an instantaneous pulsewidth of roughly
2.5 nanoseconds which enables a resolution to better than 30
centimeters.
[0114] Referring again to FIG. 1, the object to be located has an
attached RF location tag 102, preferably a tag having a UWB
transmitter, that transmits a signal comprising a burst (e.g., 72
pulses at a burst rate of 1 Mb/s), and optionally, a burst having a
tag data packet that may include tag data elements that may
include, but are not limited to, a tag unique identification number
(tag UID), other identification information, a sequential burst
count, stored tag data, or other desired information for object or
personnel identification, inventory control, etc. In some
embodiments, the tag data packet may include a tag-individual
correlator that can be used to associate a specific individual with
a specific tag. In some examples, the sequential burst count (e.g.,
a packet sequence number) from each tag 102 may be advantageously
provided in order to permit, at a receiver hub 108, correlation of
time of arrival (TOA) measurement data from various receivers
106.
[0115] In some examples, the RF location tag 102 may employ UWB
waveforms (e.g., low data rate waveforms) to achieve extremely fine
resolution because of their extremely short pulse (i.e.,
sub-nanosecond to nanosecond, such as a 2 ns (lns up and lns down))
durations. As such, the tag data packet may be of a short length
(e.g., 72-112 bits in some example embodiments), that
advantageously enables a higher throughput and higher transmission
rates. In some examples, higher throughput and/or higher
transmission rates may result in larger datasets for filtering to
achieve a more accurate location estimate. In some examples, rates
of up to approximately 2600 updates per second can be accommodated
without exceeding regulatory requirements. Alternatively or
additionally, in some examples, the length of the tag data packets,
in conjunction with other system functionality, may also result in
a longer battery life (e.g., a 3.0 v 1 A-hr lithium cell battery
may result in a tag battery life in excess of 3.8 years).
[0116] In some examples, one or more other tags, such as a
reference tag 104, may be positioned within and/or about a
monitored area, such as monitored area 100 illustrated herein as a
football field. In some examples, the reference tag 104 may be
configured to transmit a signal that is used to measure the
relative phase (e.g., the count of free-running counters) of
non-resettable counters within the receivers 106.
[0117] One or more (preferably four or more) receivers 106 are also
at locations with predetermined coordinates within and/or around
the monitored area 100. In some examples, the receivers 106 may be
connected in a "daisy chain" fashion to advantageously allow for a
large number of receivers 106 to be interconnected over a
significant monitored area in order to reduce and simplify cabling,
reduce latency, provide power and/or the like. Each of the
receivers 106 includes a receiver for receiving transmissions, such
as UWB transmissions, and preferably, a packet decoding circuit
that extracts a time of arrival (TOA) timing pulse train,
transmitter ID, packet number and/or other information that may
have been encoded in the tag transmission signal (e.g., material
description, personal information, etc.) and is configured to sense
signals transmitted by the tags 102 and one or more reference tags
104 (if present).
[0118] Each receiver 106 includes a time measuring circuit that
measures time differences of arrival (TDOA) of tag bursts. The time
measuring circuit is phase-locked (e.g., phase differences do not
change and therefore respective frequencies are identical) with a
common digital reference clock signal distributed via cable
connection from a receiver hub 108 having a central timing
reference clock generator. The reference clock signal establishes a
common timing reference for the receivers 106. Thus, multiple time
measuring circuits of the respective receivers 106 are synchronized
in frequency, but not necessarily in phase. While there typically
may be a phase offset between any given pair of receivers in the
receivers 106, the offset is readily determined through use of a
reference tag 104. Alternatively or additionally, each receiver may
be synchronized wirelessly via virtual synchronization without a
dedicated physical timing channel.
[0119] In some example embodiments, the receivers 106 are
configured to determine various attributes of the received signal.
Since measurements are determined at each receiver 106, in a
digital format, rather than analog, signals are transmittable to
the receiver hub 108. Advantageously, because packet data and
measurement results can be transferred at high speeds to a receiver
memory, the receivers 106 can receive and process tag (and
corresponding object) locating signals on a nearly continuous
basis. As such, in some examples, the receiver memory allows for a
high burst rate of tag events (i.e., tag data packets) to be
captured.
[0120] Data cables or wireless transmissions may convey measurement
data from the receivers 106 to the receiver hub 108 (e.g., the data
cables may enable a transfer speed of 2 Mbps). In some examples,
measurement data is transferred to the receiver hub at regular
polling intervals.
[0121] As such, the receiver hub 108 determines or computes tag
location (i.e., object location) by processing TDOA measurements
related to multiple data packets detected by the receivers 106. In
some example embodiments, the receiver hub 108 may be configured to
resolve the coordinates of a tag using nonlinear optimization
techniques. The receiver hub 108 may also be referred to herein as
a locate engine or a receiver hub/locate engine.
[0122] In some examples, the system described herein may be
referred to as an "over-specified" or "over-determined" system. As
such, the receiver hub 108 may then calculate one or more valid
(i.e., most likely) locations based on a set of measurements and/or
one or more incorrect (i.e., less likely) locations. For example, a
location may be calculated that is impossible due the laws of
physics (e.g., a tag on a football player that travels more than
100 yards in 1 second) or may be an outlier when compared to other
determined locations. As such one or more algorithms or heuristics
may be applied to minimize such error.
[0123] One such algorithm for error minimization, which may be
referred to as a time error minimization algorithm, may be
described as
= j = 1 N k = j + 1 N { ( t j - t k ) - 1 c [ [ ( x - x j ) 2 + ( y
- y j ) 2 + ( z - z j ) 2 ] 1 2 - [ ( x - x k ) 2 + ( y - y k ) 2 +
( z - z k ) 2 ] 1 2 ] } 2 ##EQU00001##
[0124] where N is the number of receivers, c is the speed of light,
x.sub.j,k, y.sub.j,k and z.sub.j,k are the coordinates of the
receivers and t.sub.j,k are the arrival times received at each of
the receivers. Note that only time differences may be received at
receiver 106 in some example embodiments. The starting point for
the minimization is obtained by first doing an area search on a
coarse grid of x, y and z over an area defined by the user. This is
followed by a localized steepest descent search.
[0125] Another or second algorithm for error minimization, which
may be referred to as a distance error minimization algorithm, may
be defined by:
= j = 1 N [ [ ( x - x j ) 2 + ( y - y j ) 2 + ( z - z j ) 2 ] 1 2 -
c ( t j - t 0 ) ] 2 ##EQU00002##
[0126] where time and location differences are replaced by their
non-differential values by incorporating an additional unknown
dummy variable, t.sub.0, which represents an absolute time epoch.
The starting point for this algorithm is fixed at the geometric
mean location of all active receivers. No initial area search is
needed, and optimization proceeds through the use of a
Davidon-Fletcher-Powell (DFP) quasi-Newton algorithm in some
examples.
[0127] In order to determine the coordinates of a tag (T), in some
examples and for calibration purposes, a reference tag (e.g.,
reference tag 104) is positioned at a known coordinate position
(x.sub.T, y.sub.T, z.sub.T).
[0128] In further example embodiments, a number N of receivers
{R.sub.j: j=1, . . . , N} (e.g., receivers 106) are positioned at
known coordinates (x.sub.R.sub.j, y.sub.R.sub.j, z.sub.R.sub.j),
which are respectively located at distances, such as:
d.sub.Rj=
(x.sub.R.sub.j-x.sub.T).sup.2+(y.sub.R.sub.j-y.sub.T).sup.2+(z.sub.R.sub.-
j-z.sub.T).sup.2
[0129] from a reference tag.
[0130] Each receiver R.sub.j utilizes, for example, a synchronous
clock signal derived from a common frequency time base, such as
clock generator. Because the receivers are not synchronously reset,
an unknown, but constant offset O.sub.j exits for each receiver's
internal free running counter. The value of the offset O.sub.j is
measured in terms of the number of fine resolution count increments
(e.g., a number of nanoseconds for a one nanosecond resolution
system).
[0131] The reference tag is used to calibrate the radio frequency
locating system as follows:
[0132] The reference tag emits a signal burst at an unknown time
.tau..sub.R. Upon receiving the signal burst from the reference
tag, a count N.sub.R.sub.j as measured at receiver R.sub.1 is given
by
N.sub.R.sub.j=.beta..tau..sub.R+O.sub.j+.beta.d.sub.R.sub.j/c
[0133] where c is the speed of light and fi is the number of fine
resolution count increments per unit time (e.g., one per
nanosecond). Similarly, each object tag T.sub.i of each object to
be located transmits a signal at an unknown time .tau..sub.i to
produce a count
N.sub.i.sub.j+.beta..tau..sub.i+O.sub.j+.beta.d.sub.i.sub.j/c
[0134] at receiver R.sub.j where d.sub.i.sub.j is the distance
between the object tag T.sub.i and the receiver at receiver
R.sub.j. Note that .tau..sub.i is unknown, but has the same
constant value for receivers of all receivers R.sub.j. Based on the
equalities expressed above for receivers R.sub.j and R.sub.k and
given the reference tag information, differential offsets expressed
as differential count values are determined as follows:
N R j - N R k = ( O j - O k ) + .beta. ( d R j c - d R k c )
##EQU00003## or ##EQU00003.2## ( O j - O k ) = ( N R j - N R k ) -
.beta. ( d R j c - d R k c ) = .DELTA. j k ##EQU00003.3##
[0135] .DELTA..sub.jk is constant as long as d.sub.Rj-d.sub.Rk
remains constant, (which means the receivers and tag are fixed and
there is no multipath situation) and .beta. is the same for each
receiver. Note that .DELTA..sub.j.sub.k is a known quantity, since
N.sub.R .sub.j, N.sub.R.sub.k, .beta., d.sub.R.sub.j/c, and
d.sub.R.sub.k/c are known. That is, the differential offsets
between receivers R.sub.j and R.sub.k may be readily determined
based on the reference tag transmissions. Thus, again from the
above equations, for an object tag (T.sub.i) transmission arriving
at receivers R.sub.j and R.sub.k:
N.sub.i.sub.j-N.sub.i.sub.k=(O.sub.j-O.sub.k)+.beta.(d.sub.i.sub.j/c-d.s-
ub.i.sub.k/c)=.DELTA..sub.j.sub.k+.beta.(d.sub.i.sub.j/c-d.sub.i.sub.k/c)
Or,
d.sub.i.sub.j-d.sub.i.sub.k=(c/.beta.)[N.sub.i.sub.j-N.sub.i.sub.k-.DELT-
A.j.sub.k].
[0136] The process further includes determining a minimum error
value E.sub.i, for each object tag T.sub.i, according to the
functional relationship:
E i = min ( x , y , z ) j k > j [ ( d i j - d i k ) - ( dist ( T
x , y , z , R j ) - dist ( T x , y , z , R k ) ) ] 2 ##EQU00004##
where ##EQU00004.2## dist ( T x , y , z , R j ) = ( x R j - x ) 2 +
( y R j - y ) 2 + ( z R j - z ) 2 ##EQU00004.3##
[0137] is the Euclidean distance between point (x,y,z) and the
coordinates of the j.sup.th receiver R.sub.j. The minimization
solution (x',y',z') is the estimated coordinate position for the
i.sup.th tag at t.sub.0.
[0138] In an example algorithm, this proceeds according to:
= j = 1 N [ [ ( x - x j ) 2 + ( y - y j ) 2 + ( z - z j ) 2 ] 1 2 -
c ( t j - t 0 ) ] 2 ##EQU00005##
[0139] where each arrival time, t.sub.j, is referenced to a
particular receiver (receiver "1") as follows:
t j = 1 .beta. ( N j - N 1 - .DELTA. j k ) ##EQU00006##
[0140] and the minimization is performed over variables (x, y, z,
t.sub.0) to reach a solution (x', y', z', t.sub.0').
[0141] In some example embodiments, the location of a tag 102
(e.g., tag location data) may then be output to the receiver
processing and analytics system 110 for further processing to
advantageously provide visualizations, predictive analytics and/or
the like.
Individuals Equipped with Location Tags and Sensors
[0142] FIG. 1 shows a monitored area 100. The monitored area 100
comprises a plurality of positions at one or more time epochs. The
plurality of positions may be divided into one or more zones. Each
zone may be described by one or more coordinate systems, such as a
local NED (North-East-Down) system, a latitude-longitude system, or
even a yard line system as might be used for an American football
game. A location is a description of a position, or a plurality of
positions, within the monitored area. For example, a field marker
at the intersection of the south goal line and west out of bounds
line at Bank of America Stadium in Charlotte, N.C. could be
described as {0,0,0} in a local NED system, or 35.225336 N 80.85273
W longitude 751 ft. altitude on a latitude-longitude system, or
simply "Panthers Goal Line" in a yard line system. Because
different types of locating systems or different zones within a
single locating system may use different coordinate systems, a
Geographical Information System may be used to associate location
data.
[0143] FIGS. 2A and 2B illustrate example individuals equipped with
tags and/or sensors that are configured to transmit signals to
receivers of a RF location system. As will be apparent to one of
ordinary skill in the art in view of this disclosure, the
composition and arrangement of the tags and sensors used on a
selected individual may change based on the health, fitness,
operation, and performance parameters that are intended for
monitoring, as well as practical considerations regarding the
equipment to be worn.
[0144] FIG. 2A depicts an individual 200, shown here for exemplary
purposes as a football player, equipped with a number of tags
202a-j and sensors 203a-d. The term "sensor" as used herein refers
to any device that detects, measures, indicates or records a
parameter associated with an individual's health, fitness,
operation, or performance.
[0145] The depicted individual 200 is equipped with a plurality of
tags 202a-j (e.g., the RF location tags 102 discussed in connection
with FIG. 1) to provide robust location data for determining
information concerning the body motion kinetics of the tagged
individual 200. In particular, the individual 200 is equipped with
tags 202a and 202b positioned proximate to the individual's
shoulder area (here under shoulder pads), tags 202c and 202d
positioned in gloves proximate to the individual's hands, tags 202e
and 202f positioned in knee pads proximate to the individual's
knees, tags 202g and 202h positioned in shoes proximate to the
individual's feet, and tags 202i and 202j positioned in sleeves or
elbow pads proximate to the individual's elbows.
[0146] As discussed above, each tag may be a device configured for
transmitting a signal, for example, a UWB signal that includes a
TOA timing pulse, and optionally, a tag data packet that may
include, but is not limited to, ID information (e.g., tag UID), a
sequential burst count or other desired information. The tag
signals (e.g., blink data) may be collected and used, e.g., by the
receiver hub 108 of FIG. 1, to determine tag location data at one
or more times, which may in turn be used, e.g., by the receiver
processing and analytics system 110 of FIG. 1, to determine
location data and body motion kinetics of the tagged individual.
The tag signal may include analog and/or digital data.
[0147] While FIG. 2A illustrates an example embodiment employing
multiple tags 202a-j, one of ordinary skill in the art will readily
appreciate that more or fewer tags 202 may be used. For example, in
one embodiment where simple location of the individual is all that
is desired (as opposed to information concerning the motion of
arms, legs, or other tagged appendages), a single tag may be used
(as shown in FIG. 2B).
[0148] The individual 200 depicted in FIG. 2A is further equipped
with a plurality of sensors 203a-d. In particular, the individual
is equipped with sensor 203a, which is an accelerometer positioned
in a helmet proximate to the individual's head; sensor 203b, which
is a body temperature sensor positioned in a jersey under the
individual's arm; sensor 203c, which is a heart rate sensor
positioned in a shoulder pads breastplate proximate to the
individual's chest; and sensor 203d, which is a blood pressure
sensor positioned on an arm band proximate to the individual's arm.
Similarly, one or more sensors could be used, perhaps together with
dimensional data, to measure the motion of appendages of the
individual, to measure relative motion of appendages relative to
one or more tags, or to measure aspects of a tag, such as motion,
direction, or acceleration, temperature, etc. Dimensional data
could include anatomical dimension information such as, for
example, the measured length of individual appendages (femur, arm,
finger, wheel circumference) or combined dimensions (height,
stride, wingspan, body length), or equipment dimensions (shoe,
wristband, shirt, lance, spear, pole, bat) or other measured or
calculated dimensions as may be useful in determining expected or
actual motion.
[0149] FIG. 2B shows a second individual 250, shown here for
exemplary purposes as a runner, equipped with a tag 252 and a
number of sensors 253a-e. The depicted tag 252 is positioned on a
head band proximate to the individual's head to encourage better
transmission performance (i.e., better line of sight RF
communication) of the tag to a receiver. The individual 250 is
equipped with sensor 253a, which is an eye dilation sensor
positioned in glasses proximate to the individual's eyes; sensor
253b, which is a hydration sensor configured to monitor sweat loss
or sweat loss rate and positioned in a body suit or shirt proximate
to the individual's back; sensor 253c, which is a sensor for
measuring contextual data, such an accelerometer for measuring
acceleration, ambient temperature sensor or like for measuring
outside temperature, humidity, barometric pressure, wind speed, air
quality or composition, or the like and which is positioned in a
shirt collar proximate to the individual's neck; sensor 253d, which
is a blood pressure monitor positioned on an arm band proximate to
the individual's arm; and sensor 253e, which is a blood chemistry
sensor configured for monitoring levels of one or more of carbon
dioxide, oxygen, potassium, calcium, sodium, hematocrit,
temperature and pH and positioned on an arm band proximate to the
individual's arm.
[0150] The depicted individual 250 is further equipped with a power
supply 255 positioned on a belt proximate to the individual's
waist. The depicted power supply may be disposed in electrical
communication (perhaps through wires sewn into clothing, etc.) with
the tag and/or sensors 253a-e to provide primary or back-up power
to such devices. In other embodiments (such as that shown in FIG.
2A), each of the tags and sensors may include their own power
supply (e.g., battery). In one embodiment, each tag includes a
battery while each of the sensors draws power from a common power
supply (not shown).
[0151] While FIGS. 2A and 2B depict a particular type of
individual, namely, athletes, various other types of individuals
are contemplated in connection with the embodiments herein
described. As will be apparent to one of ordinary skill in the art
in view of this disclosure, each individual may be equipped with a
different array of tags and sensors. For example, and without
limitation, were the selected individual a race car for motor
sports applications, such individual may be equipped with a tag
positioned proximate to the car's roof, a fuel sensor positioned
proximate a fuel tank, a temperature sensor, an RPM sensor, etc.,
positioned within the car's engine compartment, and a brake sensor
positioned proximate to the car's brake system.
[0152] In various embodiments (including those shown in FIGS. 2A
and 2B), each sensor may include a transmitter for transmitting a
sensor signal comprising, for example, a sensor information packet
to one or more receivers (such as receivers 106 of FIG. 1). The
sensor information packet may include, but is not limited to, a
sensor unique identification number (sensor UID), other
identification information, stored sensor data, one or more
environmental measurements, or other desired information for object
or personnel identification. The sensor information packet may
include analog information, digital data, or both. In some
embodiments, the sensor information packet may include a
sensor-individual correlator that can be used to associate a
specific individual with a specific sensor. In one embodiment,
multiple sensors may share a common transmitter that buffers and
transmits the sensor information packet from various sensors at
regular intervals or, in alternate embodiments, when interrogated
by a remote receiver.
[0153] In still other embodiments, one or more sensors may be
disposed in wired or wireless communication with one or more tags
and, thus, may leverage the transmitters of the one or more tags to
package and relay the sensor information packet to one or more
remote receivers. In an embodiment where a tag transmits the sensor
information packet to one or more remote receivers, the tag may
incorporate the sensor information packet into the tag-data packet,
such that the transmitted tag-data packet may include both the
sensor information packet and tag data elements. In some
embodiments, the transmitted tag-data packet may include a
tag-sensor correlator that can be used to associate a specific
sensor with a specific tag.
[0154] As may be appreciated by one skilled in the art in view of
this disclosure, the one or more sensors may be configured
specifically for attachment to an individual as a worn sensor. For
example, one or more sensors may be sewn or woven into fabric. For
a participant in a sporting event, that fabric could be part of a
uniform or referee outfit. For an individual in a sausage race, the
sensor could be attached to the lederhosen or sombrero portion of
the sausage costume. For a fan, the sensor might be worn as a
wristband or nametag. In another embodiment, one or more sensors
may be inserted under a layer of skin or swallowed. In another
example embodiment, one or more sensors may be part of a computing
application or large medical diagnostic device that the individual
patient sits by or lays next to in order to provide physical
data.
[0155] The one or more sensors may be powered wirelessly by an RF
interrogator or may be powered by a dedicated battery or capacitor
mounted within or proximate to the sensor itself. In another
embodiment, power may be provided by wire or wirelessly by a
location tag, a separate power source worn on the athlete's body,
or by environmental activity, such as solar power, kinetic energy,
shock, or motion. In another embodiment, power may be provided from
a wall outlet. In an embodiment in which power is not internal to
the sensor or provided wirelessly, power may be carried to the
sensor over the skin, via a cable, or via a woven fabric.
[0156] As will be apparent to one of ordinary skill in the art in
view of this disclosure, once the tags and sensors of FIGS. 2A and
2B are positioned on individuals, they may, in some embodiments, be
correlated to such individuals. For example, in some embodiments,
unique tag or sensor identifiers (unique IDs such as tag UIDs or
sensor UIDs) may be correlated to an individual profile (e.g., John
Smith--80 year old male patient, Fred Johnson--28 year old
triathlete, Cinco--chorizo costume with sombrero, or ID 027--race
car number 27) and stored to a remote database accessible to the
receiver processing and analytics system as discussed in greater
detail below. Each individual profile may further include or be
correlated with a variety of data including, but not limited to,
biometric data (e.g., height, weight, health data, etc.), ambient
sensor data, and other data that may be apparent to one of skill in
the art in view of the foregoing description. In some embodiments,
the tags or sensors may be associated with an individual before
they are positioned on an individual, such as when a nametag is
printed or when a RF locating tag is placed on shoulder pads which
will likely be worn by a particular athlete.
Tag ID and Sensor Data Transmission Architecture
[0157] FIGS. 3A, 3B, 3C, 3D, and 3E show block diagrams of various
different architectures that may be utilized in transmitting
signals from one or more tags and sensors to one or more receivers
of a receiver processing and analytics system in accordance with
embodiments of the invention. In some embodiments, the depicted
architectures may be used in connection with the receiver
processing and analytics system 110 of FIG. 1. More than one of
these architectures may be used together in a single system.
[0158] FIG. 3A shows a RF location tag 102, such as that shown in
FIG. 1, which may be configured to transmit a tag signal to one or
more receivers 106. The one or more receivers 106 may transmit a
receiver signal to the receiver hub/locate engine 108.
[0159] The depicted RF location tag 102 may generate or store a tag
UID and/or tag data as shown. The tag data may include useful
information such as the installed firmware version, last tag
maintenance date, configuration information, and/or a
tag-individual correlator. The tag-individual correlator may
comprise data that indicates that a monitored individual is
associated with the RF location tag 102 (e.g., name, uniform number
and team, biometric data, tag position on individual, i.e., right
wrist). As will be apparent to one of skill in the art in view of
this disclosure, the tag-individual correlator may be stored to the
RF location tag 102 when the tag is registered or otherwise
associated with an individual. While shown as a separate field for
illustration purposes, one of ordinary skill in the art may readily
appreciate that the tag-individual correlator may be part of any
tag data or even omitted from the tag.
[0160] The tag signal data transmitted from RF location tag 102 to
receiver 106 may include "blink data" as it is transmitted at
selected intervals. This "blink rate" may be set by the tag
designer or the system designer to meet application requirements.
In some embodiments, the blink rate is consistent for one or all
tags and, in other embodiments, the blink rate may data dependent
(i.e., change based on the data transmitted). Blink data includes
characteristics of the tag signal that allow the tag signal to be
recognized by the receiver 106 so the location of the RF location
tag 102 may be determined by the locating system. Blink data may
also comprise one or more tag data packets. Such tag data packets
may include any data from the tag 102 that is intended for
transmission such as, for example in the depicted embodiment, a tag
UID, tag data, and a tag-individual correlator. In the case of TDOA
systems, the blink data may be or include a specific pattern, code,
or trigger that the receiver 106 (or downstream receiver processing
and analytics system) detects to identify that the transmission is
from a RF location tag 102 (e.g., a UWB tag).
[0161] The depicted receiver 106 receives the tag signal, which
includes blink data and tag data packets as discussed above. In one
embodiment, the receiver 106 may pass the received tag signal
directly to the receive hub/locate engine 108 as part of its
receiver signal. In another embodiment, the receiver 106 could
perform some basic processing on the received tag signal. For
instance, the receiver could extract blink data from the tag signal
and transmit the blink data to the receive hub/locate engine 108.
The receiver could transmit a time measurement to the receive
hub/locate engine 108 such as a TOA measurement and/or a TDOA
measurement. The time measurement could be based on a clock time
generated or calculated in the receiver, it could be based on a
receiver offset value as explained at least in paragraph [00135]
above, it could be based on a system time, and/or it could be based
on the time difference of arrival between the tag signal of the RF
location tag 102 and the tag signal of a RF reference tag (e.g.,
tag 104 of FIG. 1). The receiver 106 could additionally or
alternatively determine a signal measurement from the tag signal
(such as a received signal strength indication (RSSI), a direction
of signal, signal polarity, or signal phase) and transmit the
signal measurement to the receive hub/locate engine 108.
[0162] FIG. 3B shows a RF location tag 202 and sensor 203, such as
those worn on an individual's person as shown in FIG. 2, which may
be configured to transmit tag signals and sensor signals,
respectively, to one or more receivers 106, 166. The one or more
receivers 106, 166 may then transmit receiver signals to the
receiver hub/locate engine 108. One or more receivers 106, 166 may
share physical components, such as a housing or antenna.
[0163] The depicted RF location tag 202 may comprise a tag UID and
tag data (such as a tag-individual correlator) and transmit a tag
signal comprising blink data as discussed in connection with FIG.
3A above. The depicted sensor 203 may generate and/or store a
sensor UID, additional stored sensor data (e.g., a
sensor-individual correlator, sensor type, sensor firmware version,
last maintenance date, the units in which environmental
measurements are transmitted, etc.), and environmental
measurements. The "additional stored sensor data" of the sensor 203
may include any data that is intended for transmission, including
but not limited to a RF location tag 202, a reference tag (e.g.,
104 of FIG. 1), a sensor receiver, a receiver 106, and/or the
receiver/hub locate engine 108.
[0164] The sensor-individual correlator may comprise data that
indicates that a monitored individual is associated with the sensor
203 (e.g., name, uniform number and team, biometric data, sensor
position on individual, i.e., right wrist). As will be apparent to
one of skill in the art in view of this disclosure, the
sensor-individual correlator may be stored to the sensor 203 when
the sensor is registered or otherwise associated with an
individual. While shown as a separate field for illustration
purposes, one of ordinary skill in the art may readily appreciate
that the sensor-individual correlator may be part of any additional
stored sensor data or omitted from the sensor altogether.
[0165] Sensors such as sensor 203 that are structured according to
embodiments of the invention may sense or determine one or more
environmental conditions (e.g. temperature, pressure, pulse,
heartbeat, rotation, velocity, acceleration, radiation, position,
chemical concentration, voltage) and store or transmit
"environmental measurements" that are indicative of such
conditions. To clarify, the term "environmental measurements"
includes measurements concerning the environment proximate the
sensor including, without limitation, ambient information (e.g.,
temperature, position,humidity, etc.) and information concerning an
individual's health, fitness, operation, and/or performance.
Environmental measurements may be stored or transmitted in either
analog or digital form and may be transmitted as individual
measurements, as a set of individual measurements, and/or as
summary statistics. For example, temperature in degrees Celsius may
be transmitted as {31}, or as {33, 32, 27, 22, 20, 23, 27, 30, 34,
31}, or as {27.9}. In some embodiments, the sensor-individual
correlator could be determined at least in part from the
environmental measurements.
[0166] In the depicted embodiment, RF location tag 202 transmits a
tag signal to receiver 106 and sensor 203 transmits a sensor signal
to sensor receiver 166. The sensor signal may comprise one or more
sensor information packets. Such sensor information packets may
include any data or information from the sensor 203 that is
intended for transmission such as, for example in the depicted
embodiment, sensor UID, additional stored sensor data,
sensor-individual correlator, and environmental measurements. A
receiver signal from receiver 106 and a sensor receiver signal from
sensor receiver 166 may be transmitted via wired or wireless
communication to receiver hub/locate engine 108 as shown.
[0167] FIG. 3C depicts a sensor 203 communicating through a RF
location tag 202 in accordance with various embodiments. In one
embodiment, the sensor 203 may be part of (i.e., reside in the same
housing or assembly structure) of the RF location tag 202. In
another embodiment, the sensor 203 may be distinct from (i.e., not
resident in the same housing or assembly structure) the RF location
tag 202 but configured to communicate wirelessly or via wired
communication with the RF location tag 202.
[0168] In one embodiment, the RF location tag 202, the sensor 203,
or both, may generate and/or store a tag-sensor correlator that
indicates an association between a RF location tag 202 and a sensor
203 (e.g., tag UID/sensor UID, distance from tag to sensor in a
particular stance, set of sensors associated with a set of tags,
sensor types associated with a tag, etc.). In the depicted
embodiment, both the RF location tag 202 and the sensor 203 store
the tag-sensor correlator.
[0169] In the depicted embodiment, sensor 203 transmits a sensor
signal to RF location tag 202. The sensor signal may comprise one
or more sensor information packets as discussed above. The sensor
information packets may comprise the sensor UID, a
sensor-individual correlator, additional stored sensor data, the
tag-sensor correlator, and/or the environmental measurements. The
RF location tag 202 may store some portion or some or all of the
sensor information packets locally and may package the sensor
information packets into one or more tag data packets for
transmission to receiver 106 as part of a tag signal or simply pass
them along as part of its tag signal.
[0170] FIG. 3D illustrates an example communication structure for a
reference tag 104 (e.g., reference tag 104 of FIG. 1), an RF
location tag 202, a sensor 203, and two receivers 106 in accordance
with one embodiment. The depicted reference tag 104 is a RF
location tag and thus may include tag data, a tag UID, and/or the
like and is capable of transmitting tag data packets. In some
embodiments, the reference tag 104 may form part of a sensor and
may thus be capable of transmitting sensor information packets.
[0171] The depicted sensor 203 transmits a sensor signal to RF
reference tag 104. The RF reference tag 104 may store some portion
or some or all of the sensor information packets locally and may
package the sensor information packets into one or more tag data
packets for transmission to receiver 106 as part of a tag signal,
or simply pass them along as part of its tag signal.
[0172] As was described above in connection with FIG. 1, the
receivers 106 of FIG. 3D are configured to receive tag signals from
the RF location tag 202 and the reference tag 104. Each of these
tag signals may include blink data, which may comprise tag UIDs,
tag data packets, and/or sensor information packets. The receivers
106 each transmit receiver signals via wired or wireless
communication to the receiver hub/locate engine 108 as shown.
[0173] FIG. 3E illustrates an example communication structure
between an RF location tag 202, a plurality of receivers 106, and a
variety of sensor types including, without limitation, a sensor
203, a diagnostic device 233, a triangulation positioner 243, a
proximity positioner 253, and a proximity label 263 in accordance
with various embodiments. In the depicted embodiment, none of the
sensors 203, 233, 243, 253 form part of an RF location tag 202 or
reference tag 104. However, each may comprise a sensor UID and
additional stored sensor data. Each of the depicted sensors 203,
233, 243, 253 transmits sensor signals comprising sensor
information packets.
[0174] In the depicted embodiment, receiver 106 is configured to
receive a tag signal from RF location tag 202 and a sensor signal
directly from sensor 203. In such embodiments, sensor 203 may be
configured to communicate in a communication protocol that is
common to RF location tag 202 as will be apparent to one of
ordinary skill in the art in view of this disclosure.
[0175] FIG. 3E depicts one type of sensor referred to herein as a
"proximity interrogator". The proximity interrogator 223 can
include circuitry operative to generate a magnetic,
electromagnetic, or other field that is detectable by a RF location
tag 202. While not shown in FIG. 3E, a proximity interrogator 223
may include a sensor UID and other sensor data or information as
discussed above.
[0176] In some embodiments, the proximity interrogator 223 is
operative as a proximity communication device that can trigger a RF
location tag 202 (e.g., when the RF location tag 202 detects the
field produced by the proximity interrogator 223) to transmit blink
data under an alternate blink pattern or blink rate. The RF
location tag can initiate a preprogrammed (and typically faster)
blink rate to allow more location points for tracking an
individual. In some embodiments, the RF location tag may not
transmit a tag signal until triggered by the proximity interrogator
223. In some embodiments the RF location tag 202 may be triggered
when the RF location tag 202 moves near (e.g., within communication
proximity to) a proximity interrogator 223. In some embodiments,
the RF location tag may be triggered when the proximity
interrogator 223 moves near to the RF location tag 202. In other
embodiments, the RF location tag 202 may be triggered when a button
is pressed or a switch is activated on the proximity interrogator
223 or on the RF location tag itself. For example, a proximity
interrogator 223 could be placed at the start line of a racetrack.
Every time a car passes the start line, a car-mounted RF location
tag 202 senses the signal from the proximity interrogator and is
triggered to transmit a tag signal indicating that a lap has been
completed. As another example, a proximity interrogator 223 could
be placed at a Gatorade cooler. Each time an athlete fills a cup
from the cooler an athlete-mounted RF location tag 202 senses the
signal from the proximity interrogator and is triggered to transmit
a tag signal indicating that Gatorade has been consumed. As another
example, a proximity interrogator 223 could be placed on a medical
cart. When paramedics use the medical cart to pick up an athlete
and move her to the locker room, an athlete-mounted RF location tag
202 senses the signal from the proximity interrogator and is
triggered to transmit a tag signal indicating that they have been
removed from the game. As explained, any of these post-triggered
tag signals may differ from pre-triggered tag signals in terms of
any aspect of the analog and/or digital attributes of the
transmitted tag signal.
[0177] FIG. 3E depicts another type of sensor that is generally not
worn by an individual but is referred to herein as a "diagnostic
device". However, like other sensors, diagnostic devices may
measure one or more environmental conditions and store
corresponding environmental measurements in analog or digital
form.
[0178] While the depicted diagnostic device 233 is not worn by an
individual, it may generate and store a sensor-individual
correlator for association with environmental measurements taken in
connection with a specific individual. For example, in one
embodiment, the diagnostic device 233 may be a blood pressure meter
that is configured to store as environmental measurements blood
pressure data for various individuals. Each set of environmental
measurements (e.g., blood pressure data) may be stored and
associated with a sensor-individual correlator.
[0179] The depicted diagnostic device 233 is configured to transmit
a sensor signal comprising sensor information packets to a sensor
receiver 166. The sensor information packets may comprise one or
more of the sensor UID, the additional stored data, the
environmental measurements, and/or the sensor-individual correlator
as discussed above. The sensor receiver 166 may associate some or
all of the data from the sensor information packets with other
stored data in the sensor receiver 166 or with data stored or
received from other sensors, diagnostic devices, RF location tags
102, or reference tags. The sensor receiver 166 transmits a sensor
receiver signal to a receiver hub/locate engine 108.
[0180] Another type of sensor shown in FIG. 3E is a triangulation
positioner 243. A "triangulation positioner" is a type of sensor
that senses position. The depicted triangulation positioner 243
includes a sensor UID, additional stored sensor data, and
environmental measurements as discussed above.
[0181] In some embodiments, a triangulation positioner (also known
as a global positioning system (GPS) receiver) receives clock data
transmitted by one or more geostationary satellites (a satellite in
a known or knowable position) and/or one or more ground based
transmitters (also in known or knowable positions), compares the
received clock data, and computes a "position calculation". The
position calculation may be included in one or more sensor
information packets as environmental measurements.
[0182] In another embodiment, a triangulation positioner comprises
one or more cameras or image-analyzers that receive emitted or
reflected light or heat, and then analyzes the received images to
determine the location of an individual or sensor. Although a
triangulation positioner may transmit data wirelessly, it is not a
RF location tag because it does not transmit blink data or a tag
signal that can be used by a receiver hub/locate engine 108 to
calculate location. In contrast, a triangulation positioner senses
position and computes a position calculation that may then be used
as environmental measurements by the receiver hub/locate engine
108.
[0183] In one embodiment, a triangulation positioner could be
combined with a RF location tag or reference tag (not shown). In
such embodiments, the triangulation positioner could compute and
transmit its position calculation via the RF location tag to one or
more receivers. However, the receiver hub/locate engine would
calculate tag location based on the blink data received as part of
the tag signal and not based solely on the position calculation.
The position calculation would be considered as environmental
measurements and may be included in associated sensor information
packets.
[0184] As will be apparent to one of ordinary skill in the art,
position calculations (e.g., GPS receiver position calculations)
are not as accurate as the location calculations (e.g., UWB
waveform based location calculations) performed by receiver
hub/locate engines structured in accordance with various
embodiments of the invention. That is not to say that position
calculations may not be improved using known techniques. For
example, a number of influences, including atmospheric conditions,
can cause GPS accuracy to vary over time. One way to control this
is to use a differential global positioning system (DGPS)
comprising one or a network of stationary triangulation positioners
that are placed in a known position, and the coordinates of the
known position are stored in memory as additional stored sensor
data. These triangulation positioners receive clock data from
geostationary satellites, determine a position calculation, and
broadcast a difference between the position calculation and the
stored coordinates. This DGPS correction signal can be used to
correct for these influences and significantly reduce location
estimate error. Exemplary use of DGPS correction may be found in
commonly owned U.S. Pat. No. 7,755,541, which is hereby
incorporated by reference herein in its entirety.
[0185] Another type of sensor shown in FIG. 3E is a proximity
detector 253. A "proximity detector" is a type of sensor that
senses identity within an area (e.g., a local area) that is small
with respect to the monitored area 100 of FIG. 1. Many different
ways of sensing identity (e.g., a unique ID or other identifier for
a sensed object or individual) would be apparent to one of ordinary
skill in the art in view of this disclosure including, without
limitation, reading a linear bar code, reading a two-dimensional
bar code, reading a near field communication (NFC) tag, reading a
RFID tag such as a UHF tag, HF tag, or low frequency tag, an
optical character recognition device, a biometric scanner, or a
facial recognition system.
[0186] In some embodiments, a proximity detector senses an
attribute of an individual (or an individual's wristband, tag,
label, card, badge, clothing, uniform, costume, phone, ticket,
etc.). The identity sensed by a proximity detector may be stored
locally at the proximity detector 253 as shown and transmitted as
environmental measurements via one or more sensor information
packets to a sensor receiver 166.
[0187] In some embodiments, a proximity detector 253 may have a
defined position, which is often stationary, and may be associated
with a location in the monitored area 100 of FIG. 1. For example, a
proximity detector 253 could be located at a finish line of a race
track, an entrance gate of a stadium, with a diagnostic device, at
a goal line or goal post of a football field, at a base or home
plate of a baseball diamond, or a similar fixed location. In such
embodiments where the proximity detector is stationary, the
position coordinates of the proximity detector and a sensor UID
could be stored to a monitored area database (not shown) that is
accessible by one or more of the receivers 106, 166, the receiver
hub/locate engine 108, and/or other components of the receiver
processing and analytics system 110. In embodiments where the
proximity detector is movable, a position calculation could be
determined with a triangulation positioner, or the proximity
detector could be combined with a RF location tag and located by
the receiver hub/locate engine 108. While shown as separate fields
for illustration purposes in FIG. 3E, identify information and
position calculation could comprise part of the additional stored
sensor data, the environmental measurements, or both.
[0188] In one embodiment, the proximity detector could be
associated with a reference tag (e.g., tag 104 of FIG. 1) whose
position is recorded in the monitored area database. In other
embodiments, the proximity detector is movable, such that it may be
transported to where it is needed. For example, a proximity
detector 253 could be located on a medical cart, first down marker,
a diagnostic device, go kart, forklift, or carried by a paramedic
or security guard. In an embodiment where the proximity detector
253 is movable it would typically be associated with a RF location
tag or triangulation positioner so that location (for a RF location
tag) or position (for a triangulation positioner) can be determined
at the time identity is sensed.
[0189] In the embodiment where the proximity detector includes a RF
location tag, the receiver hub/locate engine 108 would locate the
associated RF location tag, and the tag data/sensor data filter 112
would associate the tag location data for the associated RF
location tag as the position of the proximity detector, while
determining the identity of an associated individual from any
received sensor information packets. In the alternate embodiment
where the proximity detector includes a triangulation positioner,
the triangulation positioner would compute a position calculation
that could be stored as additional stored sensor data and/or
environmental measurements, and transmitted as one or more sensor
information packets. In one embodiment, sensor information packets
for a proximity detector may include both sensed identity
information and a position calculation.
[0190] Another type of sensor shown in FIG. 3E is a proximity label
263. A proximity label has a fixed position and an identification
code (e.g., a sensor UID). The proximity label 263 may further
comprise additional stored sensor data as shown. The depicted
proximity label 263 is configured to be read by proximity detector
253. In some embodiments, proximity detector 253 may be further
configured to write information to proximity label 263.
[0191] A proximity label 263 may be a sticker, card, tag, passive
RFID tag, active RFID tag, NFC tag, ticket, metal plate, electronic
display, electronic paper, inked surface, sundial, or otherwise
visible or machine readable identification device as is known in
the art. The coordinates of the position of the proximity label 263
are stored such that they are accessible to the receive hub/locate
engine 108. For example, in one embodiment, the position
coordinates of a proximity label 263 could be stored in a field
database or monitored area database accessible via a network, or
stored locally as additional stored data in the proximity detector
253.
[0192] In some embodiments, a position of the proximity label 263
is encoded into the proximity label 263 itself. For example,
coordinates of a position of the proximity label 263 could be
encoded into a passive RFID tag that is placed in that position. As
another example, the coordinates of a position of the proximity
label 263 could be encoded into a printed barcode that is placed in
that position. As another example, a proximity label 263 comprising
a NFC tag could be encoded with the location "end zone", and the
NFC tag could be placed at or near an end zone at Bank of America
stadium. In some embodiments, the stored coordinates of the
proximity label 263 may be offset from the actual coordinates of
the proximity label 263 by a known or determinable amount.
[0193] In one embodiment, a proximity label 263 such as an NFC tag
may be encoded with a position. When a sensor such as a proximity
detector approaches the NFC tag it may read the position, then
transmit the position in a sensor information packet to the sensor
receiver 166' and eventually to the receiver hub/locate engine 108.
In another embodiment, a proximity label 263 such as a barcode
label may be encoded with an identification code. When a smartphone
with a proximity detector (such as a barcode imager) and a
triangulation positioner (such as a GPS chip, GPS application, or
similar device) approaches the barcode label it may read the
identification code from the barcode, determine a position
calculation from received clock data, then transmit the identity
and the position calculation to sensor receiver 166' and eventually
to the receiver hub/locate engine 108 as part of one or more sensor
information packets.
[0194] In the depicted embodiment, triangulation positioner 243 and
proximity detector 253 are each configured to transmit sensor
signals carrying sensor information packets to sensor receiver
166'. The depicted sensors 243, 253, like any sensor discussed
herein, may transmit sensor signals via wired or wireless
communication protocols. For example, any proprietary or standard
wireless protocol (e.g., 802.11, Zigbee, ISO/IEC 802.15.4, ISO/IEC
18000, IrDA, Bluetooth, CDMA, or any other protocol) could be used
for the sensor signals. Alternatively or additionally, any standard
or proprietary wired communication protocol (e.g., Ethernet,
Parallel, Serial, RS-232, RS-422, USB, Firewire, I.sup.2C, etc.)
may be used. Similarly, sensor receiver 166', and any receiver
discussed herein, may use similar wired and wireless protocols to
transmit receiver signals to the receiver hub/locate engine.
[0195] In one embodiment, upon receiving sensor signals from the
triangulation positioner 243 and the proximity detector 253, the
sensor receiver 166' may associate some or all of the data from the
received sensor information packets with other data stored to the
sensor receiver 166', or with data stored or received from other
sensors (e.g., sensor 203), diagnostic devices 233, RF location
tags 102, or RF reference tags 104. Such associated data is
referred to herein as "associated sensor data". In the depicted
embodiment, the sensor receiver 166' is configured to transmit some
or all of the received sensor information packets and any
associated sensor data to the receiver hub/locate engine 108 at
part of a sensor receiver signal.
[0196] In one embodiment, a smartphone comprising a proximity
detector (such as a barcode imager) and a triangulation positioner
(such as a GPS chip) may associate an identification code
determined from a barcode with a position calculation from received
clock data as associated sensor data and transmit a sensor
information packet that includes such associated sensor data to the
receiver hub/locate engine 108. In another embodiment, the
smartphone could transmit a first sensor information packet
including the identification code and the smartphone's unique
identifier to another sensor receiver, the smartphone could
transmit a second sensor information packet including the position
calculation and the smartphone's unique identifier to the sensor
receiver, and the sensor receiver could associate the position
calculation with the identification code based on the common
smartphone unique identifier and transmit such associated sensor
data to the receiver hub/locate engine 108. In another embodiment,
the sensor receiver could determine a first time measurement
associated with the first sensor information packet and a second
time measurement associated with the second sensor information
packet that, in conjunction with the sensor UID, could be used, by
the receiver hub/locate engine 108, to associate the first sensor
information packet with the second sensor information packet.
[0197] In one embodiment, the receiver hub/locate engine 108
receives receiver signals from the receiver 106 and sensor receiver
signals from the sensor receivers 166, 166'. In the depicted
embodiment, receiver 106 may receive blink data from the RF
location tag 102 and transmits to the receiver hub/locate engine
108 some or all of the blink data, perhaps with additional time
measurements or signal measurements. In some embodiments, time
measurements or signal measurements may be based on a tag signal
received from a RF reference tag (e.g., reference tag 104 of FIG.
1). The receiver hub/locate engine 108 collects the blink data,
time measurements (e.g. time of arrival, time difference of
arrival, phase), and/or signal measurements (e.g. signal strength,
signal direction, signal polarization, signal phase) from the
receivers 106 and computes tag location data for the tags 102 as
discussed above in connection with FIG. 1. In some embodiments, the
receivers 106 may be configured with appropriate RF filters, such
as to filter out potentially interfering signals or reflections
proximate the field of play or other area to be monitored.
[0198] The receiver hub/locate engine 108 may also access stored
data or clock data from local storage and from a network location.
The receiver hub/locate engine 108 uses this information to
determine tag location data for each RF location tag. It may also
associate data derived or extracted from tag signals transmitted
from one or more RF location tags with information or data derived
or extracted from sensor signals transmitted from one or more
sensors.
[0199] In addition to the TOA or TDOA systems previously described,
other real-time location systems (RTLS) such as received signal
strength indication based systems could potentially be implemented
by a receiver hub/locate engine 108. Any RTLS system using RF
location tags, including those described herein, could require
considerable processing by the receiver hub/locate engine 108 to
determine the tag location data from the blink data received from
the tags. These may require time measurement and/or signal
measurement in addition to blink data, which preferably includes a
tag UID. In contrast, in other systems, such as global position
systems (GPS) systems, location data is determined based upon the
position calculation transmitted from a GPS transmitter (also
referred to as a GPS receiver or GPS tag) which includes calculated
information about the location where the tag was positioned (i.e.,
coordinates determined at the tag via satellite signal
triangulation, etc.) when the position calculation was determined
or stored. Thus, GPS information typically refers to additional
information that is transmitted along with a GPS transmitter ID
before the transmission is received by a sensor receiver.
[0200] A GPS host device or back-end server may receive the GPS
information and simply parse the position calculation (as opposed
to calculating the position information at the host device) and the
GPS transmitter ID into a data record. This data record may be used
as a GPS position calculation, or it could be converted to a
different coordinate system to be used as a GPS position
calculation, or it could be processed further with DGPS information
to be used as a GPS position calculation.
[0201] Returning to FIG. 3C, the depicted RF location tag 202 is
used to convey (sometimes called backhaul) sensor information
packets to a receiver 106. In some embodiments, while not shown,
multiple sensors 203 may transmit sensor signals carrying sensor
information packets to RF location tag 202. Such received sensor
information packets may be associated with blink data that is
transmitted to receiver 106.
[0202] In one embodiment, the receiver hub/locate engine 108 may
parse sensor information packets from received tag data packets and
associate such sensor information packets with the RF location tag
202 that transmitted the sensor information packet. Thus, the
receiver hub/locate engine 108 may be able to determine tag
location data, which may comprise a location and other data (e.g.,
tag data, tag UID, tag-individual correlator, sensor-individual
correlator, additional stored sensor data, environmental
measurements, tag-sensor correlator, identity information, position
calculation, etc.) from one or more tags or sensors. Such data and
information may be transmitted to the receiver processing and
analytics system 110.
[0203] In some embodiments, once the receiver hub/locate engine 108
determines a location estimate of a RF location tag 102 at the time
epoch of the tag signal, the receiver hub/locate engine 108 can
also associate a location estimate with the tag data packet
included in the blink data of such tag signal. In some embodiments,
the location estimate of the tag signal may be used as tag location
data for the tag data packet. In some embodiments a Geographical
Information System (GIS) may be used by the receive hub/locate
engine 108 to refine a location estimate, or to map a location
estimate in one coordinate system to a location estimate in a
different coordinate system, to provide a location estimate for the
tag data packet. In some embodiments the Geographical Information
System may include a known location for one or more RF reference
tags identifiable by a tag unique identification number.
[0204] In one embodiment, the location estimated for the tag data
packet may be associated with any data in the tag data packet,
including a tag UID, other tag data, and, if included, one or more
sensor information packets, including sensor UID, additional stored
sensor data, and environmental measurements. Since environmental
measurements may include a position calculation from a
triangulation positioner (e.g., a GPS device), the receiver
hub/locate engine 108 could parse the position calculation and use
it to refine a location estimate for the tag data packet.
[0205] Preferably, the receiver hub/locate engine 108 may access an
individual database to determine tag-individual correlators or
sensor-individual correlators. Individual data (e.g., an individual
profile) may be stored in a server, in tag memory, in sensor
memory, or in other storage accessible via a network or
communication system, including tag data or additional stored
sensor data as explained previously.
[0206] In some embodiments, by comparing data accessed using a
sensor-individual correlator, the receiver hub/locate engine 108
may associate an individual with a sensor information packet
received from a sensor, and/or may associate an individual with
such sensor. Because the receiver hub/locate engine 108 may
associate a sensor position estimate with a sensor information
packet, the receiver hub/locate engine 108 may also estimate an
individual position for the associated individual.
[0207] In another embodiment, by comparing data accessed using a
tag-sensor correlator, the receiver hub/locate engine 108 may
associate a sensor with a tag data packet received from a RF
location tag 102. Because the receiver hub/locate engine 108 may
associate a location estimate with a tag data packet, the receiver
hub/locate engine 108 may also create a sensor location estimate
for the associated sensor. By comparing a location estimate for a
RF location tag with a sensor location estimate or a sensor
position estimate, the receiver hub/locate engine 108 may associate
a RF location tag with a sensor, or may associate a tag data packet
with a sensor information packet. The receiver hub/locate engine
108 could also determine a new or refined tag-sensor correlator
based on this association.
[0208] In still another embodiment, by comparing a location
estimate for a RF location tag with an individual location estimate
or an individual position estimate, the receiver hub/locate engine
108 may associate a RF location tag with an individual, or may
associate a tag data packet with an individual. The receiver
hub/locate engine 108 could also determine a new or refined
tag-individual correlator based on this association.
[0209] In one embodiment, by comparing a location estimate for a
sensor with an individual location estimate or an individual
position estimate, the receiver hub/locate engine 108 may associate
a sensor with an individual, or may associate a sensor information
packet with an individual. The receiver hub/locate engine 108 could
also determine a new or refined sensor-individual correlator based
on this association.
[0210] Data derived or extracted from tag signals transmitted from
one or more RF location tags is referred to herein as "tag derived
data" and shall include, without limitation, tag data, tag UID,
tag-individual correlator, tag-sensor correlator, tag data packets,
blink data, time measurements (e.g. time of arrival, time
difference of arrival, phase), signal measurements (e.g., signal
strength, signal direction, signal polarization, signal phase) and
tag location data (e.g., including tag location estimates).
Information or data derived or extracted from sensor signals
transmitted from one or more sensors is referred to herein as
"sensor derived data" and shall include, without limitation, sensor
UID, additional stored sensor data, sensor-individual correlator,
environmental measurements, sensor information packets, position
calculations (including sensor position estimates), position
information, identity information, tag-sensor correlator, and
associated sensor data. Data derived or extracted from stored
individual data is referred to herein as "individual profile
information" and shall include, without limitation, tag-individual
correlator, sensor-individual correlator, identity information,
name, uniform number and team, biometric data, tag position on
individual. In various embodiments, the receiver hub/locate engine
108 may transmit tag derived data, sensor derived data, individual
profile information, various combinations thereof, and/or any
information from the GIS, the field database, the monitored area
database, and the individual database to the receiver processing
and analytics system 110.
Receiver Processing and Analytics System
[0211] FIGS. 4A and 4B are block diagrams of two systems that may
be specifically configured in accordance with an example embodiment
of the present invention. As shown in FIG. 4A, receiver hub/locate
engine 108 may be configured to access or receive receiver signals
comprising tag derived data and sensor derived data from one or
more receivers. In one example embodiment, the receiver hub/locate
engine 108 may access or provide a data transmission link to each
of one or more receivers in succession and download, for example, a
plurality of TOA measurements, blink data, and sensor derived data
having been buffered in the receiver since the receiver hub/locate
engine 108 last accessed the data.
[0212] Receiver hub/location engine 108 may further be configured
to provide tag location data and/or sensor derived data and/or
individual profile information to the receiver processing and
analytics system 110. The receiver processing and analytics system
110 may include a tag data/sensor data filter 112 configured for
receiving the tag location data and sensor derived data and
individual profile information from the receiver hub 108.
[0213] The tag data/sensor data filter 112 may be configured for
associating tag derived data (including tag location data) and
sensor derived data to a particular individual, filtering tag
derived data from sensor derived data, and routing the tag location
data and the sensor derived data to an individual dynamics/kinetics
engine 120 and an HFOP engine 116, respectively.
[0214] The tag data/sensor filter 112 accesses an individual role
database 114 to identify historical or contextual information
related to the individual and/or role data for the individual
(e.g., data indicating that a first individual is a particular
football player in football game application, data indicating that
a second individual is a particular patient in a hospital
application, data indicating that a third individual is a race car
in a motor sports application, etc.) In another embodiment,
historical information related to the health, fitness, operation
and/or performance of an individual may be provided by the
individual role database 114. For example, a medical history may be
provided for a particular individual by the individual role
database 114.
[0215] The individual role database 114 may be populated with
information (e.g., tag-individual correlators, sensor-individual
correlators, tag-sensor correlators, etc.) that allows the tag
data/sensor filter 112 to determine whether location tags and
sensors are to be associated with or correlated to a particular
individual. In one embodiment, the location tags and/or sensors
comprise a unique identifier (e.g., tag UID, sensor UID) that is
transmitted to the receiver hub/locate engine 108 and passed to the
tag data/sensor data filter 112. That tag data/sensor data filter
112 uses the unique identifiers to correlate the tag and sensor
derived data to individual profiles (e.g., individual data) stored
to the individual role database 114. In another embodiment, the
individual role database 114 is configured to associate the sensors
and/or sensor derived data with a particular individual, which will
be described in FIGS. 12A-12C.
[0216] The individual role database 114 may further include role
information associated with individuals, location tags, and/or
sensors. For example, particular individuals may be associated with
a particular role (e.g., John Doe/quarterback).
[0217] The tag data/sensor data filter 112 may then be configured
to filter tag derived data (including tag location data) from
sensor derived data and provide sensor derived data to a health,
fitness, operation and performance engine 116 (HFOP engine 116) and
provide tag derived data to an individual dynamics/kinetics engine
120.
[0218] The HFOP engine 116 may be configured to receive sensor
derived data from the tag data/sensor data filter 112. In one
embodiment, the HFOP engine 116 may be further configured to
receive historical or contextual information related to the
individual and/or a role of the individual. The HFOP engine 116 may
further be configured to access a sensor based HFOP models database
118. The sensor based HFOP models database 118 may be populated
with historical sensor derived data related to individuals (e.g., a
baseline snapshot of individual health parameters suggesting an
individual is healthy, sick, injured, or the like). The historical
data may be generated from capturing sensor derived data from an
individual equipped with sensors. The sensor based HFOP models
database 118 may, additionally or alternatively, be populated
manually or programmatically populated with health related data
from patient studies, machine diagnostics studies, and the
like.
[0219] In one embodiment, historical information related to the
health, fitness, operation and/or performance of a particular
individual may be provided by the HFOP models database 118. That
is, the HFOP models database 118 may include at least one of health
history data, fitness history data, operation level history data,
or performance level history data for an individual. The health
history data, fitness history data, operation level history data,
or performance level history data for the individual may be
manually input (e.g., inputting that a particular individual has
hypertension) or may be acquired by prior monitoring, thus
capturing tag derived data and/or sensor derived data from a
previous monitoring session.
[0220] In some embodiments, the HFOP engine 116 may aggregate data
related to one or more sensors for a particular individual over a
period of time (e.g., heart rate data for one play for a football
player in a football game, sweat rate data for a runner during a
race, blood pressure data for an elderly patient on a walk, etc.).
Using the historical and contextual data related to the individual
and/or role of the individual, the HFOP engine 116 may further be
configured to access the sensor based HFOP models database 118 and
determine a list of probable HFOP statuses. For example, high heart
rate data for a football player during a play may indicate that the
player currently engaged in a high level of exertion (e.g., HFOP
status 1) or perhaps is experiencing the onset of a stroke (e.g.,
HFOP status 2). The HFOP engine 116 uses other sensor derived data
from other sensors to determine whether HFOP status 1 is more or
less probable than HFOP status 2. Such information is fed to the
HFOP status engine 124 as discussed in greater detail below.
[0221] In one embodiment, the HFOP engine 116 may be configured to
output one or more probable outcomes or alternative selected data
of interest. In another embodiment, the HFOP engine 116 may also be
configured to output the aggregated sensor derived data, the
historical and contextual data related to the individual and/or the
role of the individual related to one or more probable outcomes or
selected data of interest. In still another embodiment, the HFOP
engine 116 may be configured to output various health parameters
for the individual per unit time.
[0222] The individual dynamics/kinetics engine 120 may be
configured to receive tag derived data (including tag location
data) from the tag data/sensor data filter 112. In one embodiment,
the individual dynamics/kinetics engine 120 may further be
configured to receive historical or contextual information related
to the individual and/or a role of the individual. The individual
dynamics/kinetics engine 120 may further be configured to access an
individual dynamics/kinetics models database 122.
[0223] The individual dynamics/kinetics engine 120 may compare the
tag location data and individual role data to individual
dynamics/kinetics models to determine aspects of the individual
dynamics or movement kinetics. The dynamics/kinetics model database
122 may comprise models of different aspects or dimensions that may
be based on past tag location data (e.g., tags fixed to various
appendages of a monitored individual) or other data generated by a
model generation engine or other tools. The models may include,
without limitation, models for a particular individual profile
(e.g., John Smith), an individual type (e.g., quarterback, a horse,
a stock car, etc.), and the like. Such models may consider all
three dimensions (x, y, z) of the tag location data for each tag
(e.g., 202 of FIG. 2A) and may further consider different tag
position arrays (e.g., two tag implementations--one proximate each
shoulder, eleven tag implementations--one proximate each shoulder,
one proximate each elbow, one proximate each hand, one proximate
each knee, one proximate each foot, and one proximate to the head,
etc.).
[0224] The individual dynamics/kinetics models database 122 may be
populated with actual tag data drawn from particular actions (e.g.,
running on a track, running in a soccer game, walking, throwing,
running, limping, falling, spraining an ankle or the like). The
historical data may be generated from capturing data from an
individual equipped with location tags or sensors, performing
actions and storing the data with the associated action. The
individual dynamics/kinetics models database 122 may, additionally
or alternatively, be populated with average or aggregate data that
may indicate particular actions. In another embodiment, position
history data, such as historical information related to the dynamic
and/or kinetic information of a particular individual (e.g., John
Smith), may be stored and provided by the individual
dynamics/kinetics models database 122.
[0225] In one embodiment, the individual dynamics/kinetics engine
120 determines a multi-dimensional individual location per unit
time (e.g., individual location data) for each individual based on
the tag location data, the individual role data, and the individual
dynamics/kinetics models. Such multi-dimensional individual
location data may include (1) relative position of the individual
relative to a monitored area (e.g., a field of play, race track,
etc.), (2) general orientation of the individual (e.g., standing,
squatting, laying the ground, sitting, etc.), and (3) a specific
orientation of the individual and/or various appendances of the
individual (e.g., preparing to pass, in a three-point stance, in a
ball-carrying position, in a tackling position, negotiating a turn,
etc.).
[0226] The individual dynamics/kinetics engine 120 uses the real
time tag location data stream from the tag data/sensor data filter
112, as well as the individual role data, to provide accurate
information about what a particular individual is doing in real
time. In some embodiments, the individual dynamics/kinetics engine
120 may further use sensor derived data, received from the tag
data/sensor data filter 112 in the depicted embodiment, to aid in
determining not only where the individual is, but also how that
individual's location is changing with time, velocity,
acceleration, deceleration, orientation, or the like. For example,
in one embodiment, the sensor derived data may comprise
accelerometer data that may indicate that an individual (or portion
of an individual) is accelerating or decelerating. The individual
dynamics/kinetics engine 120 outputs multi-dimensional individual
location data per unit time.
[0227] The HFOP engine 116 and the individual dynamics/kinetics
engine 120 are each configured to output data streams to a health,
fitness, operation and performance (HFOP) status engine 124. The
HFOP status engine 124 is configured to receive the ranked list of
probable HFOP statuses from the HFOP engine 116 and the
multi-dimensional individual location data from the individual
dynamics/kinetics engine 120 and determine health, fitness,
operation, or performance statuses for the individual. For example,
in one embodiment, the HFOP status engine 124 may determine that
the football player having the high heart rate discussed above is
likely experiencing the onset of a stroke because the received
multi-dimensional individual location data suggests the player is
sitting on a bench and has not recently undertaken a high level of
physical exertion. In such an embodiment, the HFOP status engine
124 may be configured to send an alert to medical staff.
[0228] In some embodiments, the HFOP status engine 124 may further
be configured to receive location data, sensor derived data, and
historical or contextual data related to the individual and/or role
of the individual related to the location data and associated
probable actions. Such information may be used to establish a
baseline of health, fitness, operation, or performance for a given
individual that may be stored as part of the individual's profile
data. In some embodiments, such baseline may include a baseline of
health, fitness, operation, or performance parameters.
[0229] In some embodiments, the HFOP status engine 124 may be
configured to determine a status (e.g., healthy, sick, injured,
hurt, active, not active, powered down, etc.) by comparing real
time (or near real time) data concerning an individual's health,
fitness, operation, or performance parameters to a stored snapshot
or pre-determined threshold.
[0230] In one embodiment, the HFOP status engine 124 may be
configured for determining a HFOP status for the individual based
on the comparing the tag derived data to individual
dynamics/kinetics models and based on comparing the sensor derived
data to at least one of health models, fitness models, operation
level models, or performance level models. In one embodiment, HFOP
status may be determined based on whether particular sensor derived
data satisfies particular threshold values related to health,
fitness, operation and/or performance models. HFOP status may
additionally or alternatively be based on whether particular
multidimensional individual location data satisfies particular
threshold values (e.g., perhaps values based on an individual's
baseline snapshot or generally published data regarding movements
or positions of a "healthy" individual of a certain age) related to
dynamics and/or kinetic information. As one skilled in the art
would understand, satisfying a threshold may include exceeding a
threshold in some embodiments (e.g., blood pressure may exceed
"healthy" threshold) or may include falling below a threshold for
some embodiments (e.g., sweat rate may fall below "healthy" rate
indicating possible dehydration).
[0231] In one embodiment, the HFOP status engine 124 may be
configured to associate the determined status and/or action with
another type of data (e.g., associating each play that a player
appears in with the player). In one embodiment, the HFOP status
engine 124 may be configured to monitor, store or track one or more
individuals, one or more of health, fitness, operation, or
performance of an individual or individuals, and/or one or more
zones or location systems for an individual. In another embodiment,
the HFOP status engine 124 may be configured to monitor a location
system for a pre-defined event, unexpected, and/or an abnormal
event. An event may be a particular action in a given context or
associated with particular sensor derived data.
[0232] In some embodiments, the HFOP status engine 124 may be
configured to cause an alert in response to a determination of a
pre-defined event (e.g., a stroke, an injury, etc.), unexpected
event, and/or abnormal event. In another embodiment, an alert may
be sent upon determining whether the determined HFOP status
satisfies a threshold value. In some embodiments, particular tag
derived data and/or sensor derived data may trigger an alert, and
thus bypass a status determination (e.g., heart rate approaching
zero, or `flat lining`). In another embodiment, the HFOP status
engine 124 may be configured for sending a message or action
related information to a parent, trainer, doctor, nurse, paramedic
or the like.
[0233] Determining a status (e.g., an HFOP status) can be done in
many ways. A sensor-based HFOP model database or an individual
dynamics kinetics model database can be used to collect various
models. A model may have multiple attributes that are represented
by data fields. As one skilled in the art of databases would
understand, a particular model may be selected by choosing one or
more fields of interest that correspond with field values known
from the tag derived data and/or the sensor derived data and/or
individual role data. By comparing the fields of interest with the
known field values, a status can be determined.
[0234] One way to determine compare the fields of interest with the
known field values is to match the field of interest to the known
field value.
[0235] A second way to compare the fields of interest with the
known field values is to develop a control chart. A control chart
uses a known or estimated distribution of values from a population
of observations for the field of interest to determine an average
(mean) value and a standard deviation. The model designer may set
the mean and standard deviation based on the field of interest, or
on a calculated value related to the field of interest, such as the
differential between the field of interest and a standard
multi-variable regression model estimate based on the field of
interest. Two control limits are established, an upper control
limit at three standard deviations more than the mean, and a lower
control limit at three standard deviations less than the mean
(i.e., a number other than three may be chosen by the model
designer). As known field values are determined, each can be added
to the control chart. If an observation exceeds the control limits
(either higher than the upper control limit or lower than the lower
control limit) the status can be deemed to have changed. Also, if
individual field values continue to increase or decrease at least
seven (or another value chosen by the model designer) times, the
data is said to be trending and the status can be deemed to have
changed.
[0236] A third way to compare the fields of interest with the known
field values is to cluster the values from a population of
observations for the field of interest. Various clustering
algorithms are known in the art, including incremental clustering
algorithms, divide-and-conquer based clustering algorithms, data
sampling and summarization techniques, Euclidean distance based
clustering algorithms, and kernel clustering algorithms. This may
have the benefit of significantly reducing the data storage
required, as a very large number of observations can be
characterized by a smaller number of multi-variable clusters. By
comparing known field values to variables for each cluster, a
probability that tag location data is the same status as other
observations in that cluster can be determined.
[0237] A fourth way to compare the fields of interest with the
known field values is to calculate a covariance between past
observations for the field of interest and current observations for
the known field values (e.g., tag derived data, sensor derived
data, and/or identity/role data). If the covariance is above a
threshold value (such as 0.97) the status can be matched to the
selected model.
[0238] In some embodiments, if the status is changed such that an
event is identified by the HFOP status engine 124 an alert may be
generated. In some embodiments, if the status changes from an
undetermined status to a determined status then an event may be
indicated by the HFOP engine as an alert.
[0239] FIG. 4B shows another embodiment of a system that may be
specifically configured in accordance with an example embodiment of
the present invention. As many of the components are identical or
similar to above described embodiments, repetition of the
description of such components will be omitted.
[0240] In the depicted embodiment, receiver hub/locate engine 108
receives receiver signals and outputs tag derived data (including
tag location data) and sensor derived data to a tag data/sensor
data filter 112 of a receiver processing and analytics system 110'
structured in accordance with one embodiment.
[0241] While in certain embodiments, the receiver hub/location
engine 108 is configured to correlate tag location data to tag UID
and optionally sensor UID, in the depicted embodiment, the tag
data/sensor data filter 112 may be configured to perform one or
more additional correlations or associations. For example, the tag
data/sensor data filter 112 may be configured to associate the tag
UID with an individual (e.g., Jane Smith), the tag UID with a role,
the tag location data with an individual or role, and the like.
[0242] FIGS. 5A-5E show example embodiments of individual, role,
and performance associations. FIG. 5A depicts a correlation of tag
UIDs to individuals. In particular, tag UIDs 1-4 are correlated to
individual names (e.g., Jim, Ed, and Andy). Notably, as discussed
above, in some embodiments individuals may receive multiple tags
and thus Ed is correlated to tags 2 and 3. As will be apparent to
one of skill in the art, tag UIDs need not be correlated to actual
names (i.e., alphanumeric characters) and may be correlated to
unique identifiers that are themselves correlated to individuals.
In some embodiments, sensor UIDs (not shown) may be similarly
correlated to individuals. In some embodiments, one or more
addresses or sets of instructions may be used to access tag UID and
sensor UID correlations of the type shown in FIG. 5A. Such
addresses and instructions may be referred to as tag-individual
correlators and sensor-individual correlators.
[0243] In one embodiment, the tag data/sensor data filter 112 may
associate an individual (e.g., an individual name, unique
identifier, etc.) with the tag UID while also associating sensor
derived data with the tag UID. In still other embodiments, the tag
data/sensor data filter 112 may use a registration database to
associate a tag UID with an individual.
[0244] In some embodiments, in order to monitor the health or
fitness of an individual, which may be associated with a tag UID as
shown in FIG. 5A, measured performance may be compared with
anticipated performance. As described above, performance may be
generally described as a vector of measurements or attributes,
e.g., Attribute A, Attribute B, and Attribute C.
[0245] FIG. 5B depicts a correlation between individual and
performance. For example, an actual or average performance by an
individual in connection with Attributes A, B, and C may be
correlated to each individual as shown. Additionally or
alternatively, in another embodiment, such actual or average
performance may be correlated to a role as shown in FIG. 5D. A
typical or average performance may also be correlated to individual
and to role as shown in FIG. 5E. Finally, an individual may be
correlated to one or more roles as shown in FIG. 5C. To support
such correlations, the tag data/sensor data filter 112 may be
configured to access one or both of an individual database 410 and
a role database 420, which are illustrated in FIG. 4B.
[0246] FIGS. 6A-6C show example embodiments for correlating tag
UID, individual data, role data, and performance data. FIG. 6A
illustrates how individual data (e.g., an individual identifier,
name, etc.) may be used to correlate a tag UID to performance data.
FIG. 6B illustrates how individual data (e.g., an individual
identifier) may be used to correlate tag UID, role data, and
performance data. FIG. 6C illustrates how individual data and role
data may be used to correlate tag UID, and performance data.
[0247] Returning to FIG. 4B, the depicted tag data/sensor data
filter 112 is configured to correlate tag derived data (including
tag location data) and sensor derived data to individual data from
individual database 410 and role data from role database 420. The
tag data/sensor data filter 112 is configured, inter alia, to pass
such correlated tag derived data, sensor derived data, individual
data, and role date to the individual dynamics/kinetics engine
120.
[0248] In one embodiment, the individual dynamics/kinetics engine
120 is disposed in communication with historical data store 440 and
individual dynamics/kinetics models database 122. The individual
dynamics/kinetics models database 122 may be configured to store
data related to an individual. For example, referring to the
example above related to a human athlete individual who is
standing, walking, or running, and where a tag is positioned at the
individual's shoulder (e.g., tag 202a of FIG. 2A) and foot (e.g.,
tag 202g of FIG. 2A). The models database 122 may include a stature
value of approximately four feet, which represents the distance or
height between the shoulder and foot tags. Such stature value may
be used by the system to identify, inter alia, when the individual
is bent over, seated, etc. In another embodiment, the models
database 122 may include a minimum stature value, e.g., 24 inches,
which may represent a minimum distance or height expected between
the shoulder and foot tags of even the shortest of individuals.
[0249] In one embodiment, the minimum stature value could vary by
role (e.g., tagged football players may be expected to be taller
than tagged horse racing jockeys, etc.). In another embodiment, the
individual dynamics/kinetics engine 120 may calculate a stature
value (h.sub.t) at various times (t) by subtracting the height of
tag 202g (z.sub.g) from the height of tag 202a (z.sub.a) (i.e.,
h.sub.t=z.sub.a-z.sub.g) based on tag location data from the
receiver hub/locate engine 108 and individual data from the tag
data/sensor data filter 112. In an embodiment where individual data
may be stored on the tag (e.g., 202a), the individual data may be
forwarded through the receiver hub/locate engine 108 to the tag
data/sensor data filter 112.
[0250] In one embodiment, the individual dynamics engine 120 may
store the individual stature value h.sub.t as time series data in
historical data store 440 or it could gather additional descriptive
statistics such as a mode, median, standard deviation, number of
observations for any or all of h.sub.t, z.sub.a and z.sub.g, and
then store the descriptive statistics in historical data store 440.
In one embodiment, time series data or descriptive statistics
stored in historical data store 440 may include individual data or
role data that may be used for additional context in building
individual dynamic models as stored in the individual dynamics
models database122.
[0251] In one embodiment, during monitoring, the receiver
hub/locate engine 108 may determine and collect tag derived data
(including tag location information) and sensor derived data and
provide such data to the tag data/sensor data filter 112. The tag
data/sensor data filter 112 may access and/or utilize individual
data from the individual data store 410 or role data from the role
data store 420 before providing information to the individual
dynamics engine 120.
[0252] In one embodiment, the individual dynamics engine 120 may be
configured to calculate h.sub.T=z.sub.aT-z.sub.gT for a particular
time T, and compare that to an expected value of h from the
individual dynamics models 122. The results of the comparison are
then provided to HFOP status engine 124, which may provide an alert
to a coach, medic, parent or other appropriate personnel. In one
embodiment, an alert may be provided if z.sub.aT is less than some
small fraction of measured stature (e.g., the measured distance
from the ground to a shoulder mounted tag) or if z.sub.at is less
than some fraction of minimum stature (e.g., the expended or
average distance from the ground to a shoulder mounted tag),
suggesting that the athlete is crouched down or laying down. By
considering z.sub.aT-z.sub.gT, in one embodiment, the system may
determine that an individual is bent over or laying down, even if
such individual is not on the ground.
[0253] In another embodiment, an alert may be provided if h.sub.T
is less than some fraction of descriptive statistics such as the
mode or median value of h.sub.t or z.sub.at for a selected
individual or for a compilation of individuals with a similar role.
In another embodiment, an alert may be provided if h.sub.t is less
than the mean value of h.sub.t minus 3 times the standard deviation
of h.sub.t suggesting that the recent value of h.sub.t differs from
past observations more than would be expected from random
measurement variation. In another embodiment, a control chart for h
could be constructed based on historical values of h.sub.t, and
multiple observations of h.sub.T may be compared to the control
chart. Using well-established methods of statistical process
control, a trend of increasing h.sub.T, decreasing h.sub.T, or
observations of h.sub.T beyond established control limits may be
used to initiate an alert. Although the preceding examples have
been used to describe various embodiments of the invention, it
should be appreciated that one skilled in the art may utilize
additional embodiments.
[0254] In some embodiments, the HFOP status engine 124 may be
configured to access zone data from a zone data store 430 to
determine whether or how to produce an alert. The term zone data as
used herein refers to data or other information that might define
one or more geographic or environmental areas, zones, or sectors.
Such "zones" may or may not correlate to any real-world boundaries
(e.g., a room may be divided into four zones regardless of whether
any actual boundaries, i.e., partitions, walls, etc., exist in the
room). In one embodiment, an individual may be located in one of
multiple zones in an area monitored by a RTLS system, such as the
system described with reference to FIG. 1. HFOP status engine 124
may be configured to utilize one or more rules for providing an
alert depending on zone data. For example, a first rule may apply
when an athlete is on the field of play (e.g., send an alert when
the athlete is sitting for more than 15 seconds), which may differ
from the rules for providing an alert when an athlete is on the
sidelines or when an athlete is in the clubhouse.
[0255] In some embodiments, the tag data/sensor data filter 112 may
use zone data from the zone data store 430 to determine whether the
individual (or tag(s) associated with the individual) is positioned
in a particular zone. The HFOP status engine 124 may use
information about the zone stored in zone data store 430 in
determining whether to produce an alert for any embodiment
described herein.
[0256] As discussed in connection with FIG. 4A above, the tag
data/sensor data filter 112 may be configured to filter tag derived
data (including tag location data) from sensor derived data and
provide sensor derived data to a health, fitness, operation and
performance engine 116 (HFOP engine 116) and provide tag derived
data (including tag location data) to an individual
dynamics/kinetics engine 120.
[0257] In some embodiments, sensor derived data may additionally or
alternatively be used as a determining factor by the HFOP status
engine 124. For instance, as discussed above, the HFOP status
engine sensor-based HFOP models 118 may be created and/or utilized
to show that the typical body temperature for a human athlete
individual is approximately 37 degrees Celsius. The model may be
further refined (or one or more other models provided) based on
data collected during a registration process. For example a model
may be refined in which a body temperature is measured by a sensor
(and transmitted as environment measurements) and later associated
with the individual as individual data. In another embodiment,
sensor derived data may be stored based on an individual's role,
for example body temperature may be stored based on the athlete's
role. In another embodiment, sensor derived data (e.g., body
temperature) may be stored as historical time-series data or
descriptive statistics data based on multiple sensor readings.
[0258] In one embodiment, during a monitoring process, a sensor
(e.g., a GPS equipped smartphone having a barcode imaging
application) may be capable of communicating both sensor derived
data and a position calculation or position information as in FIG.
3E, or, a sensor (e.g., a thermometer) may be configured to
communicate sensor derived data to which a tag is able to append
tag derived data as discussed in connection with FIGS. 3C and
3D.
[0259] In one embodiment, the tag data/sensor data filter 112 may
obtain tag derived data (including tag location data) and sensor
derived data from the receiver hub/locate engine 108 or from a
combination of the receiver hub/locate engine 108 and stored data
accessed from the individual data store 410, the role data store
420, and the zone data store 430. In one embodiment, the tag
data/sensor data filter 112 may provide sensor derived data (such
as the temperature measured by a thermometer) to the HFOP engine
116 where it may be utilized in a comparison to sensor-based HFOP
models accessed from the HFOP models database 118. In one
embodiment, results of the comparison may then be provided to HFOP
status engine 124, which may then provide an alert to a coach,
medic, parent or other appropriate personnel.
[0260] In another embodiment, tag location data and the relative
position of the applicable sensor or sensors (as may be determined
based on the tag location data or sensor based position
calculations or position information) may be used to determine if
the athlete's temperature was measured orally, under the arm, or
rectally. The sensor derived data and tag derived data (including
tag location data) may be used by the HFOP engine 116 to compare to
the measured temperature data to sensor-based HFOP models
correlated to body temperature measurement position. The results of
this comparison may then be provided to HFOP status engine 124,
which may provide an alert.
[0261] Again, as discussed above, individual data such as Jim or
Mary, role data such as water boy or running back, or zone data
such as Lambeau Field or sauna may also be provided by the tag
data/sensor data filter 112 to the HFOP engine 116 in order to add
context to the sensor derived data, such as temperature measured by
the thermometer.
[0262] In some embodiments, sensor derived data from sensors not
associated with the individual (e.g., taken by sensors not mounted
to the individual but still routed wired or wirelessly to the
receiver hub/locate engine 108) may also be collected by the tag
data/sensor data filter 112 and provided to the HFOP engine 116
and/or individual dynamics engine 120. For instance, an ambient
temperature thermometer sensor could provide important context to
determining an appropriate body temperature at the HFOP engine 116
or determining an appropriate amount of time spent by the water
cooler to the individual dynamics engine 120.
Health, Fitness, Operation, and Performance Monitoring
[0263] FIGS. 7, 8, 9, 10, 11, 12A-12C, and 14 illustrate example
flowcharts of the example operations performed by a method,
apparatus and computer program product in accordance with an
embodiment of the present invention. It will be understood that
each block of the flowcharts, and combinations of blocks in the
flowcharts, may be implemented by various means, such as hardware,
firmware, processor, circuitry and/or other devices associated with
execution of software including one or more computer program
instructions.
[0264] For example, in reference to FIG. 15, one or more of the
procedures described herein may be embodied by computer program
instructions. In this regard, the computer program instructions
which embody the procedures described above may be stored by a
memory 1524 of an apparatus employing an embodiment of the present
invention and executed by a processor 1522 in the apparatus.
[0265] As will be appreciated by one of ordinary skill in the art,
any such computer program instructions may be loaded onto a
computer or other programmable apparatus (e.g., hardware) to
produce a machine, such that the resulting computer or other
programmable apparatus provides for implementation of the functions
specified in the flowcharts' block(s). These computer program
instructions may also be stored in a non-transitory
computer-readable storage memory that may direct a computer or
other programmable apparatus to function in a particular manner,
such that the instructions stored in the computer-readable storage
memory produce an article of manufacture, the execution of which
implements the function specified in the flowcharts' block(s). The
computer program instructions may also be loaded onto a computer or
other programmable apparatus to cause a series of operations to be
performed on the computer or other programmable apparatus to
produce a computer-implemented process such that the instructions
which execute on the computer or other programmable apparatus
provide operations for implementing the functions specified in the
flowcharts' block(s).
[0266] As such, the operations of FIGS. 7, 8, 9, 10, 11, 12A-12C,
and 14 when executed, convert a computer or processing circuitry
into a particular machine configured to perform an example
embodiment of the present invention. Accordingly, the operations of
FIGS. 7, 8, 9, 10, 11, 12A-12C, and 14 define an algorithm for
configuring a computer or processing to perform an example
embodiment. In some cases, a general purpose computer may be
provided with an instance of the processor which performs the
algorithms of FIGS. 7, 8, 9, 10, 11, 12A-12C, and 14 to transform
the general purpose computer into a particular machine configured
to perform an example embodiment.
[0267] Accordingly, blocks of the flowcharts support combinations
of means for performing the specified functions and combinations of
operations for performing the specified functions. It will also be
understood that one or more blocks of the flowcharts, and
combinations of blocks in the flowcharts, can be implemented by
special purpose hardware-based computer systems which perform the
specified functions, or combinations of special purpose hardware
and computer instructions.
[0268] In some embodiments, certain ones of the operations herein
may be modified or further amplified as described below. Moreover,
in some embodiments additional optional operations may also be
included. It should be appreciated that each of the modifications,
optional additions or amplifications below may be included with the
operations above either alone or in combination with any others
among the features described herein.
Receiver Processing and Analytics System Processes
[0269] FIG. 7 shows an example method that may be executed by one
or more machines (some examples of which are discussed in
connection with FIGS. 1, 2, 4A, 4B, and 15) to monitor the health,
fitness, operation, or performance of individuals, in accordance
with some embodiments discussed herein.
[0270] As shown in block 710 of FIG. 7, an apparatus, such as a
receiver processing and analytics system 110 or 110', may be
configured for accessing, capturing, and/or receiving data from a
receiver hub/locate engine. In one embodiment, tag derived data may
be received from at least one location tag located on or near an
object or individual. In yet another embodiment, tag derived data
may be downloaded from a memory (e.g., hard drive, thumb drive or
the like).
[0271] As shown in block 720 of FIG. 7, an apparatus, such as
receiver processing and analytics system 110 or 110', may be
configured for accessing, capturing, and/or receiving data from a
receiver hub/locate engine 108. In one embodiment, sensor derived
data may be received from at least one sensor located on or near an
object or person. In yet another embodiment, sensor derived data
may be downloaded from a memory (e.g., hard drive, thumb drive or
the like).
[0272] As shown in block 730 of FIG. 7, an apparatus, such as
receiver processing and analytics system 110 or 110' and/or, more
specifically an HFOP engine, may be configured for determining a
probable list of HFOP statuses related to a health, fitness,
operation, and performance of individual or object based on
associated sensor derived data. In one embodiment, historical
and/or contextual information may be utilized. In another
embodiment, sensor based health, fitness, operation, and/or
performance models may be accessed and/or utilized in the
determination. In one embodiment, one or more probable statuses
related to the health, fitness, operation and/or performance of an
individual may be determined.
[0273] As shown in block 740 of FIG. 7, an apparatus, such as
receiver processing and analytics system 110 and/or, more
specifically an individual dynamics/kinetics engine, may be
configured to determine aspects of the individual dynamics or
movement kinetics including, without limitation, individual
location data. In one embodiment, the apparatus may be configured
for determining at least one status related to individual dynamics
or movement kinetics based on associated individual location data.
In one embodiment, historical and/or contextual information may be
utilized. In another embodiment, the dynamics/kinetics model
database 122 may comprise models of different aspects or dimensions
that may be utilized. In one embodiment, at least one probable
status or a list of probable statuses related to the individual
dynamics or movement kinetics of an individual may be
determined.
[0274] As shown in block 750 of FIG. 7, an apparatus, such as
receiver processing and analytics system 110 and/or, more
specifically an HFOP status engine, may be configured for
determining at least one status from a probable list of statuses
based on at least one of an HFOP status (or probable list of HFOP
statuses) related to a health, fitness, operation, and performance
of an individual and a status (or probable list of statuses)
related to individual dynamics or movement kinetics. In one
embodiment, historical and/or contextual information may be
utilized in the determination of a status.
[0275] As shown in block 760 of FIG. 7, an apparatus, such as
receiver processing and analytics system 110 and/or, more
specifically an HFOP status engine, maybe configured for causing an
output. The output may include one of an HFOP status of an
individual, multi-dimensional location data related to a particular
individual, and an alert in an instance in which the HFOP status
satisfies a threshold value or is determined to be one of a number
of predefined HFOP statuses. For example, an output may include
transmitting data to a processing system that displays action
related data (e.g., a graphical user interface for tracking or
monitoring one or more individuals).
[0276] In another embodiment, the apparatus may be configured for
causing an alert in response to determining a presence of a
particular status, such as for example, unexpected status, an
abnormal status, or a pre-defined status (e.g., stroke, injury,
etc.).
[0277] In one embodiment, that apparatus may be configured for
sending a message and/or status related information to a parent,
trainer, doctor, nurse, paramedic or the like. The message may
indicate, for example, that help is requested and/or required. The
message and/or information may comprise the location of the first
individual. In another embodiment, the message or information may
comprise information related to the abnormal or unexpected status,
or the pre-defined status that was determined.
Tag Data/Sensor Data Filter Processing
[0278] FIG. 8 shows an example method that may be executed by one
or more machines (some examples of which are discussed in
connection with FIGS. 1, 4A and 4B) to separate tag derived data
and sensor derived data, associate such data with an individual and
provide the data to the appropriate processing engine, in
accordance with some embodiments discussed herein.
[0279] As shown in block 810 of FIG. 8, an apparatus, such as a tag
data/sensor data filter 112 or receiver processing and analytics
system 110 or 110', may be configured for accessing, capturing,
and/or receiving data from a receiver hub/locate engine 108. In
another embodiment, the data may be packetized data including one
or both of tag derived data (including tag location data) and
sensor derived data. The data may be associated with a capture
time. In another embodiment, tag derived data and/or sensor derived
data may be downloaded from a memory (e.g., hard drive, thumb drive
or the like).
[0280] As shown in block 820 of FIG. 8, an apparatus, such as a tag
data/sensor data filter 112 or receiver processing and analytics
system 110 or 110', may be configured for separating the tag
derived data (e.g., tag location data) and the sensor derived
data.
[0281] As shown in block 830 of FIG. 8, an apparatus, such as a tag
data/sensor data filter 112 or a sensor processing and distribution
system 110 or 110', may be configured for associating the tag
and/or sensor derived data with a particular individual.
[0282] The association may be performed by identifying an
identifier (e.g., a tag-individual correlator, sensor-individual
correlator, etc.) included in the packetized data associated with
particular tag derived data and/or sensor derived data. The
identifier may then be checked against individual identifier data
by accessing an individual role database. The individual role
database 114, as described earlier, may include data associating
unique identifiers to particular individuals, and/or particular
individuals to particular roles.
[0283] As shown in block 840 of FIG. 8, an apparatus, such as a tag
data/sensor data filter 112 or receiver processing and analytics
system 110 or 110', may be configured for accessing historical or
contextual information related to the individual and/or a role of
the individual.
[0284] As shown in block 850 of FIG. 8, an apparatus, such as a tag
data/sensor data filter 112 or a sensor processing and distribution
system 110 or 110', may be configured for providing sensor derived
data and associated information, such as the historical or
contextual information related to the individual and/or a role of
the individual, to a HFOP engine 116.
[0285] As shown in block 860 of FIG. 8, an apparatus, such as a tag
data/sensor data filter 112 or receiver processing and analytics
system 110 or 110', may be configured for providing tag derived
data and associated information, such as the historical or
contextual information related to the individual and/or a role of
the individual, to an individual dynamics/kinetics engine 120.
Health, Fitness, Operation, and Performance Engine Processing
[0286] FIG. 9 shows an example method that may be executed by one
or more machines (some examples of which are discussed in
connection with FIGS. 1, 4A and 4B) to determine one or more
probable actions associated with sensor derived data related to an
individual, in accordance with some embodiments discussed
herein.
[0287] As shown in block 910 of FIG. 9, an apparatus, such as a
HFOP engine 116 or receiver processing and analytics system 110 or
110', may be configured for accessing, capturing, and/or receiving
sensor derived data. In some embodiments, the apparatus may be
configured for receiving sensor derived data, and associated
information, such as the historical or contextual information
related to an individual and/or a role of the individual.
[0288] As shown in block 920 of FIG. 9, an apparatus, such as a
HFOP engine 116 or receiver processing and analytics system 110 or
110', may be configured for accessing a sensor based HFOP models
database 118 and identifying associated models. As described
earlier, the sensor based HFOP models database 118 may be populated
with historical sensor derived data related to individuals (e.g., a
baseline snapshot of individual health parameters suggesting an
individual is healthy, sick, injured, or the like). HFOP models
database 118 may include at least one of health history data,
fitness history data, operation level history data, or performance
level history data for an individual.
[0289] Identifying historical sensor derived data and/or associated
models may be performed by comparing sensor derived data and
associated information received from an individual to sensor
derived data and associated information related to a historical
sensor derived data and/or models comprising a combination of
historical sensor derived data (e.g., heart rate, breathing rate
etc. related to a stroke). In some circumstances, the comparison
may yield a match with a particular model. In other circumstances,
no precise match is identified but the comparison identifies one or
more models most likely associated with the received sensor derived
data and associated information (e.g., individual data, role data,
etc.).
[0290] As shown in block 930 of FIG. 9, an apparatus, such as a
HFOP engine 116 or receiver processing and analytics system 110 or
110', may be configured for performing a comparison between sensor
derived data, and optionally associated information, and one or
more HFOP models. The HFOP engine 116 may be configured to
aggregate data related to one or more sensors for a particular
individual over a period of time and utilize the aggregated data in
the comparison.
[0291] As shown in block 940 of FIG. 9, an apparatus, such as a
HFOP engine 116 or receiver processing and analytics system 110 or
110', may be configured for determining one or more probable
statuses related to the sensor derived data and optionally, the
associated data, based on the comparison of block 930.
[0292] As shown in block 950 of FIG. 9, an apparatus, such as a
HFOP engine 116 or receiver processing and analytics system 110 or
110', may be configured for providing one or more probable statuses
to a HFOP status engine 124. In some embodiments, one or more
probable actions are provided in conjunction with the related
sensor derived data and associated data.
Individual Dynamics/Kinetics Engine Processing
[0293] FIG. 10 shows an example method that may be executed by one
or more machines (some examples of which are discussed in
connection with FIGS. 1 and 2) to provide multi-dimensional
individual location information per unit time, in accordance with
some embodiments discussed herein.
[0294] As shown in block 1010 of FIG. 10, an apparatus, such as an
individual dynamics/kinetics engine 120 or receiver processing and
analytics system 110 or 110', may be configured for accessing,
capturing, and/or receiving tag derived data (including tag
location data). In some embodiments, the apparatus may be
configured for receiving tag derived data, and associated
information, such as the historical or contextual information
related to an individual and/or a role of the individual.
[0295] As shown in block 1020 of FIG. 10, an apparatus, such as an
individual dynamics/kinetics engine 120 or receiver processing and
analytics system 110 or 110', may be configured for accessing an
individual dynamics/kinetics model database 122 and identifying
aspects of the individual dynamics or movement kinetics and/or
associated models.
[0296] Identifying associated models may be performed by comparing
tag derived data and associated information received from an
individual to tag derived data and associated information related
to a model. While no model may be a match, the apparatus may be
configured to identify similar models or those most closely
associated with a particular individual, position, or role.
[0297] As shown in block 1030 of FIG. 10, an apparatus, such as an
individual dynamics/kinetics engine 120 or receiver processing and
analytics system 110 or 110', may be configured for performing a
comparison between tag derived data (including tag location data)
and one or more individual dynamics/kinetics models. In some
embodiments, a comparison is performed utilizing the associated
information.
[0298] In one example, the individual dynamics/kinetics engine 120
is configured to determine a multi-dimensional individual location
per unit time (e.g., individual location data) for an individual
based on the tag location data, the individual role data, and the
individual dynamics/kinetics models. Such multi-dimensional
individual location may include (1) relative position of the
individual relative to a monitored area (e.g., a field of play,
race track, etc.) or zone, (2) general orientation of the
individual (e.g., standing, squatting, laying the ground, sitting,
etc.), and (3) a specific orientation of the individual including
the orientation of one or more appendages (e.g., preparing to pass,
in a three-point stance, in a ball-carrying position, in a tackling
position, negotiating a turn, etc.).
[0299] The individual dynamics/kinetics engine 120 uses the real
time tag location data stream from the tag data/sensor data filter
112, as well as the individual role data to provide accurate
information about what a particular individual is doing in real
time. Thus, as shown in block 1040 of FIG. 10, an apparatus, such
as an individual dynamics/kinetics engine 120 or receiver
processing and analytics system 110 or 110', may be configured for
determining one or more probable actions, or optionally
non-actions, related to the individual dynamics or movement
kinetics of the individual.
[0300] As shown in block 1050 of FIG. 10, an apparatus, such as an
individual dynamics/kinetics engine 120 or receiver processing and
analytics system 110 or 110', may be configured for providing
multi-dimensional individual position information per unit time
(e.g., individual location data). In one embodiment, the individual
dynamics/kinetics engine 120 may be configured for providing one or
more probable actions to a HFOP status engine 124. In some
embodiments, one or more probable actions are provided in
conjunction with the related tag data and associated data.
Health, Fitness, Operation, and Performance Engine Processing
[0301] FIG. 11 shows an example method that may be executed by one
or more machines (some examples of which are discussed in
connection with FIGS. 1. 4A, and 4B) to determine a HFOP status of
an individual based on tag data and sensor derived data related to
the individual, in accordance with some embodiments discussed
herein.
[0302] As shown in block 1110 of FIG. 11, an apparatus, such as an
HFOP status engine 124 or receiver processing and analytics system
110 or 110', may be configured for accessing, capturing, and/or
receiving data. In some embodiments, the apparatus may be
configured for receiving one or more probable actions and/or
multi-dimensional individual location data per unit time determined
utilizing tag data, and associated information and/or receiving one
or more probable statuses determined utilizing sensor derived data,
and associated information. In some embodiments, the apparatus may
further be configured for receiving the tag location data (or other
tag derived data) associated with each of the one or more probable
actions, the sensor derived data associated with each of one or
more probable statuses, and/or associated information, such as the
historical or contextual information related to an individual
and/or a role of the individual. In one embodiment, the one or more
statuses are ranked in order of likelihood based on the comparison
in the HFOP engine 116.
[0303] As shown in block 1120 of FIG. 11, an apparatus, such as an
HFOP status engine 124 or receiver processing and analytics system
110 or 110', may be configured for determining an HFOP status.
[0304] HFOP status determination may be made utilizing the one or
more probable actions and/or the multi-dimensional individual
location data per unit time determined by the individual
dynamics/kinetics engine 120 utilizing tag data, and associated
information and/or the one or more probable statuses determined by
the HFOP engine 116 utilizing sensor derived data, and associated
information. In some embodiments, HFOP status determination may
utilize one or more of the tag location data associated with each
of the one or more probable actions, the sensor derived data
associated with each of one or more probable statuses, and/or
associated information, such as the historical or contextual
information related to an individual and/or a role of the
individual.
[0305] As shown in block 1130 of FIG. 11, an apparatus, such as an
HFOP status engine 124 or receiver processing and analytics system
110 or 110', may be configured for providing HFOP status related
information. In one embodiment, HFOP status related information may
be provided to a processing system for storing and/or displaying
real time (or near real time) HFOP status related information.
Additionally or alternatively, real time or near real time action
data, multi-dimensional location data and/or sensor derived data
may be provided for storage or viewing.
[0306] In some embodiment, as shown in block 1140 of FIG. 11, an
apparatus, such as an HFOP status engine 124 or receiver processing
and analytics system 110 or 110', may be configured for causing an
alert in response to a determination of a pre-defined HFOP status,
unexpected HFOP status, and/or abnormal HFOP status. In one
embodiment, the HFOP status engine 124 may be configured for
sending a message or HFOP status related information to a parent,
trainer, doctor, nurse, paramedic or the like.
Associating Location and Sensor Derived Data To a Specific
Individual
[0307] In some embodiments, the receiver processing and analytics
system 110 or 110' may be configured to send a message or HFOP
status related information to a parent, trainer, doctor, nurse,
paramedic or the like indicating that help is requested and/or
required. The message and/or information may comprise information
indicating where the individual is (e.g., the tag location data)
and/or the HFOP status. In another embodiment, the message may
comprise particular sensor derived data or tag derived data
(including tag location data) related to the determined HFOP
status.
[0308] In one embodiment of the present invention, to utilize a
person's medical history in conjunction with tag derived data or
sensor derived data to monitor the health and fitness of the
person, data received from location tags and/or sensors may be
associated with a particular individual. FIG. 12A shows an example
method that may be executed by one or more machines (some examples
of which are discussed in connection with FIGS. 1, 4A and 4B) to
associate data from one or more location tags or sensors with a
particular individual, in accordance with some embodiments
discussed herein.
[0309] As shown in block 1202 of FIG. 12A, an apparatus, such as a
receiver processing and analytics system 110, may be configured for
accessing, capturing or receiving sensor derived data associated
with one or more sensors. In one embodiment, as discussed above,
such sensor derived data is received from a receiver hub/locate
engine. As shown in block 1204 of FIG. 12, an apparatus, such as a
receiver processing and analytics system 110 or 110', may be
configured for extracting or determining one or more
sensor-individual correlators from the sensor derived data.
[0310] As shown in block 1206 of FIG. 12, an apparatus, such as a
receiver processing and analytics system 110 or 110', may be
configured for looking up or determining individual information
(e.g., an individual identifier, individual profile information,
individual location models, etc.). As shown in block 1208 of FIG.
12A, an apparatus, such as a receiver processing and analytics
system 110 or 110', may be configured for associating the sensor
derived data with the individual information.
[0311] FIG. 12B shows another example method that may be executed
by one or more machines (some examples of which are discussed in
connection with FIGS. 1, 4A and 4B) to associate sensor derived
data from one or more sensors with a particular individual where
only an individual and corresponding location tag UID are known in
accordance with some embodiments discussed herein.
[0312] As shown in block 1210 of FIG. 12, an apparatus, such as a
receiver processing and analytics system 110 or 110', may be
configured for accessing, capturing or receiving sensor derived
data from one or more sensors. As shown in block 1212 of FIG. 12B,
an apparatus, such as a receive hub/locate engine 108, may be
configured for determining a location of the sensors. For example,
in one embodiment, the sensor may be a triangulation positioner
that is configured to determine a position calculation. In another
embodiment, the sensor may be a proximity label having a position
encoded therein. In each of these embodiments, the respective
position calculations and position information may be used by the
receiver hub/locate engine 108 to determine a location within the
monitored area 100. This location is sent from the receiver
hub/locate engine 108 and received by the receiver processing and
analytics system 110 or 110' as part of the sensor derived
data.
[0313] As shown in block 1214 of FIG. 12B, an apparatus, such as a
receiver processing and analytics system 110 or 110', may be
configured for accessing, capturing or receiving tag derived data
from one or more location tags. As shown in block 1216 of FIG. 12B,
an apparatus, such as a receiver processing and analytics system
110 or 110', may be configured for extracting or determining a
location tag UID from the tag derived data captured from one or
more location tags.
[0314] As shown in block 1218 of FIG. 12B, an apparatus, such as a
receiver processing and analytics system 110 or 110', may be
configured for extracting or determining tag location data for each
of the one or more location tags. As shown in block 1220 of FIG.
12B, an apparatus, such as a receiver processing and analytics
system 110 or 110', may be configured for determining which of one
or more location tags are most proximately located to the locations
of the sensors associated with the sensor derived data.
[0315] As shown in block 1222 of FIG. 12B, an apparatus, such as a
receiver processing and analytics system 110 or 110', may be
configured for identifying an individual associated with location
tags (e.g., perhaps based on tag-individual correlators, etc.)
determined to be closest to the sensors of interest.
[0316] As shown in block 1224 of FIG. 12B, an apparatus, such as a
receiver processing and analytics system 110 or 110', may be
configured for associating sensor derived data with the individual
identified based on his/her association with one or more location
tags. For example, sensor derived data having been captured and
determined to be captured from a first location is associated with
one or more location tags identified as associated with an
individual located nearest the first location.
[0317] In another embodiment, it may be useful to capture sensor
derived data from one or more sensors associated with or near a
particular location tag and packetize the data for sending to a
receiver, processing system or the like. FIG. 12C shows an example
method that may be executed by one or more machines (some examples
of which are discussed in connection with FIGS. 1, 4A and 4B) to
capture and packetize sensor derived data from one or more sensors,
in accordance with some embodiments discussed herein.
[0318] As shown in block 1226 of FIG. 12C, an apparatus, such as a
receiver processing and analytics system 110 or 110', may be
configured for accessing, capturing or receiving data from a
receiver. The data may comprise tag derived data and sensor derived
data from one or more location tags and one or more sensors, each
location tag and/or sensor having a unique identifier (e.g., a tag
UID, a sensor UID, etc.). As shown in block 1228 of FIG. 12C, an
apparatus, such as a receiver processing and analytics system 110
or 110', may be configured for extracting one or more location tag
identifiers from the tag derived data.
[0319] As shown in block 1230 of FIG. 12C, an apparatus, such as a
receiver hub/locate engine 108, may be configured for determining a
location (i.e., tag location data) of the one or more RF location
tags from one or more receiver signals. As shown in block 1232 of
FIG. 12C, an apparatus, such as a receive hub/locate engine 108 or
a receiver processing and analytics system 110 or 110', may be
configured for decoding the data payload of the tag derived data
and the sensor derived data to determine an individual identity. As
shown in block 1234 of FIG. 12C, an apparatus, such as a receive
hub/locate engine 108 or a receiver processing and analytics system
110 or 110', may be configured for accessing a database to
determine associated information, such as for example individual
information associated with the location tags (e.g., perhaps based
on tag-individual correlators, tag UIDs identified at block 1232).
As shown in block 1236 of FIG. 12C, an apparatus, such as a receive
hub/locate engine 108 or a receiver processing and analytics system
110 or 110', may be configured for associating the tag derived
data, and the sensor derived data to the associated information,
such as the individuals associated with any one or more of the
received data.
First Example Embodiment
[0320] In one example embodiment, FIG. 13 shows a first individual
1310, a second individual 1320 and an observer 1330 (e.g., a
coach). In a situation where the observer 1330 is close enough, and
he or she may be able to tell that the first individual 1310 is not
ok (e.g., does not satisfy a predetermined HFOP threshold) and the
second individual 1320 is ok (e.g., does satisfy a predetermined
HFOP threshold) based on the fact that the first individual 1310 is
on the ground 1340 and the second individual 1320 is standing.
However, in this case, the observer 1330 may have to leave the
scene to find help.
[0321] In one exemplary embodiment of the present invention, where
the first individual 1310 is wearing first location tag 1350 on,
for example, their shoulder or chest area, a receiver processing
and analytics system 110 or 110' may be configured to utilize tag
derived data from the location tag to determine if the first
individual 1310 is ok (e.g. not on the ground, unmoving).
[0322] In one example embodiment, a comparison of tag derived data
from the first location tag 1350 with one or more individual
dynamics/kinetics models may determine one or more probable
actions. In one embodiment, a HFOP status may be determined based
on the comparison. If the determined status exceeds a threshold,
and thereby for example, indicates a presence of an abnormality, an
alert may be caused. For example, one HFOP model may indicate a
falling action if tag location data indicates a person is on the
ground.
[0323] In another example embodiment, a receiver processing and
analytics system 110 or 110' may be configured to utilize tag
derived data from the location tag and historical data and/or
contextual data to determine if the first individual 1310 is ok.
For example, where the first individual 1310 is on the ground for a
short period of time, a comparison of tag derived data from the
first location tag 1350 in conjunction with historical and/or
contextual data may be associated with one or more probable
actions, as determined by HFOP or individual dynamics/kinetics
models that indicate that there is no problem. However, in some
embodiments, when the first individual 1310 is on the ground for a
pre-defined period of time, a different HFOP or individual
dynamics/kinetics model may be implicated, which may yield a
determination of a different action (e.g., injured).
[0324] In another example, the first individual may be equipped
with two location tags, such as for example, a first location tag
1350, 1350' on their chest or shoulder area and a second location
tag 1360, 1360' on their feet area. A receiver processing and
analytics system 110 or 110' may be configured to utilize data from
the first location tag 1350 and the second location tag 1360 to
determine if the first individual 1310 is ok. For example, based on
a height or distance between the location tag 1350, 1350' and the
second location tag 1360, 1360', the receiver processing and
analytics system 110 or 110' may determine that the first
individual 1310 is not ok and the second individual 1320 is ok,
because for example the height or distance between the first
location tag 1350 and the second location tag 1360 on the first
individual 1310 is less than a predetermined threshold. Whereas on
the second individual 1320, the first location tag 1350' is greater
than a predetermined threshold above the second location tag
1360'.
Second Example Embodiment
[0325] In one example embodiment of the present invention,
historical and contextual data, such as an individual's medical
history data, may be utilized in conjunction with location data and
sensor derived data to monitor the health and fitness of a
person.
[0326] FIG. 14 shows an example method that may be executed by one
or more machines (some examples of which are discussed in
connection with FIGS. 1, 4A and 4B) to monitor the health and
fitness of individuals using real time data processing and
factoring in associated historical data, in accordance with some
embodiments discussed herein.
[0327] As shown in block 1410 of FIG. 14, an apparatus, such as
receiver processing and analytics system 110 or 110', may be
configured for accessing, capturing, and/or receiving tag derived
data based on at least one location tag located on or near an
object or person.
[0328] As shown in block 1420 of FIG. 14, an apparatus, such as
receiver processing and analytics system 110 or 110', may be
configured for accessing, capturing, and/or receiving sensor
derived data based on at least one sensor located on or near an
individual such as an object, person or animal.
[0329] In one embodiment, where the individual is a human being or
an animal, as discussed above, a sensor may be one of a blood
pressure sensor for measuring blood pressure, a heart rate sensor
for measuring heart rate, a body temperature sensor for measuring
body temperature, a microphone (e.g., other sensors configured to
determining or representing sounds, etc.) for determining sounds
such as speech, whimpers, or screams, a mental response sensor for
measuring a mental response time, an eye dilation sensor for
measuring dilation of the eyes, and a breathing rate sensor for
measuring a breathing rate.
[0330] In another embodiment, where the individual is an object
such as a car, a sensor may be one of a throttle position sensor,
brake position sensor, steering position sensor, axle, wheel or
drive shaft sensor, rotary position sensor, crankshaft position
sensor, engine coolant temperature sensor, water temperature
sensor, oil temperature sensor, fuel gauge sensor, oil gauge
sensor, suspension travel sensor, accelerometer, pressure
sensor.
[0331] In some embodiments, where for example, a human being or an
animal is associated with an object, such as a race car, sensors
may be provided from both embodiments above, such as for example
throttle position sensor, ambient temperature sensor and the like
in a race car, and heart rate sensor, sweat rate sensor and the
like on the driver.
[0332] As shown in block 1430 of FIG. 14, an apparatus, such as
receiver processing and analytics system 110 or 110', may be
configured for associating the tag derived data from at least one
of a location tag and the sensor derived data from the at least one
sensor to a specific individual. In some embodiments, this
association may be based on tag-individual correlators,
sensor-individual correlators, and/or tag-sensor correlators.
[0333] The specific individual may have associated historic data,
such as for example, health data, medical history data or the like.
Medical history data may comprise for example, baseline
measurements for one or more of a person's blood pressure, heart
rate, mental response, eye dilation, and breathing rate. Associated
historical data may comprise, for example, flexibility related
data, top speed, acceleration, reaction time or the like.
[0334] In one embodiment, associating tag derived data and/or
sensor derived data may include capturing an identifier from the
data collected from the location tag or sensor and determining a
person that is associated with the identifier. However, other
methods may be used for associating data from a location tag or
sensor with an athlete and may not necessitate a location tag or
sensor having an identifier and/or having an athlete pre-associated
with that identifier. FIGS. 12A-12C, which are discussed above, are
flowcharts showing various example processes for associating an
individual with one or more location tags or sensors.
[0335] As shown in block 1440 of FIG. 14, an apparatus, such
receiver processing and analytics system 110 or 110', may be
configured for comparing tag location data associated with an
individual, such as multi-dimensional individual location data, to
one or more individual dynamics/kinetics models. In one embodiment,
associated historic data, contextual data and adversarial data may
be utilized.
[0336] In one embodiment, an individual (e.g., a football player)
may be equipped with a location tag on each foot. A receive
processing and analytics system 110 or 110' may be configured to
capture and store tag location data from each of the location tags.
The receiver processing and analytics system 110 or 110' may
further be configured to determine one or more attributes of the
individual utilizing the tag location data, for example, gait, a
top speed and acceleration. The receiver processing and analytics
system 110 or 110' may be configured to then monitor one or more
attributes and determine a probable action based on the currently
determined attributes (e.g., running top speed during a pass
route). The historic information may be utilized in the action
determination.
[0337] The action determination may repeatedly, over a period of
time, indicate a same or similar action for a particular individual
when the attribute data does not change or changes at a particular
level (e.g., the first individual does not slow down as the game
progresses or the first individual slows down 0.2 kilometers per
hour each quarter), which may be determined to be expected and/or
normal. Thus, a pre-defined output may include output to a user
interface (e.g., GUI) or storing to a hard drive.
[0338] However, the receiver processing and analytics system 110 or
110' may be configured to calculate, for example, a speed during a
pass route, and determine a second action if an abnormality or
unexpected calculation occurs. For example, if a current attribute
data calculation shows that a wide receiver is slowing down more
than a predetermined amount during a pass route, or if a stride
length is shorter by a predetermined amount, of if a gait is off by
a predetermined amount, the receiver processing and analytics
system 110 or 110' may be configured to determine a second action
(e.g., sprained ankle, dehydration, torn ACL, or the like).
[0339] In another embodiment, a context may be considered. A
context may be related to a particular situation, such as a
specific sport, a time of day, a weather condition (e.g.,
temperature, precipitation, humidity or the like), a game
circumstance (e.g., no huddle versus huddling by an offense in
football, or base running versus playing defense in baseball) or
the like.
[0340] For example, the receiver processing and analytics system
110 or 110' may be configured to consider or factor in contextual
information before or during action determination. In another
embodiment, action determination and in particular, sensor based
HFOP models and/or individual dynamics/kinetics models are a
function of particular contextual information (e.g., top speed in
the rain versus top speed in dry conditions).
[0341] In one example, if multi-dimensional individual location
data of a person running at top speed is determined to show that
the individual slowed down quickly and has fallen to the ground,
the receiver processing and analytics system 110 or 110' may be
configured to consider contextual information before action
determination. For example, contextual information may reveal that
the person was running on a base path or carrying a ball toward a
line of scrimmage just before going to the ground, and thus, an
action determination may yield "sliding" or "tackled". However,
alternatively, contextual information may reveal that the
individual was in open space and no one was around, thus making an
action determination based on the slowing down and falling, any one
of a number of unexpected or abnormal actions.
[0342] In another embodiment, contextual information may include
information regarding one or more zones. For example, a location
system may be divided into various zones (e.g., a football field
and a sideline area). Thus, attribute data which may be cause for
alarm in some cases, such as an individual lying down at home
plate, may not be cause for alarm in another location, such as an
individual lying down in the dugout. In a care providing facility
situation, zone division may indicate a bedroom, bathroom, family
room couch, vs. garage, kitchen, etc. For example, an individual
lying down in a bathroom may yield one action determination,
whereas an individual lying down in a bedroom may yield a second
action determination.
[0343] In another embodiment of the present invention, adversarial
data may be considered. Thus, the receiver processing and analytics
system 110 or 110' may be configured to consider or factor in
adversarial data before or during action determination. In one
embodiment, one or more historical attributes related to a first
individual may be a function of tag derived data captured from a
second individual. For example, depending on whom a second
individual is and/or what a second individual is doing, a first
individual may act differently, such as running different speeds,
turning, cutting, jumping, positioning himself differently or the
like, or in the context of race car driving, accelerating,
decelerating, taking a different line around a turn or the like. In
one embodiment, the receiver processing and analytics system 110 or
110' may be configured to measure and/or calculate relative
position information. In another embodiment, relative reaction,
speed or acceleration of a first tag (e.g., individual or body
part) relative to a second tag (e.g., a second individual or second
body part). The receiver processing and analytics system 110 or
110' may be configured to then utilize the relative attribute data
in the action determination (e.g., decreasing elbow tag position
and hand tag position may lead to an action determination of a
broken arm).
[0344] In some example embodiments, the receiver processing and
analytics system 110 or 110' may further be configured to consider
whom or what the second tag is associated with, what a second the
individual associated with the second tag position is doing, how
the individual associated with the second tag location is
performing as contextual information.
[0345] As shown in block 1450 of FIG. 14, an apparatus, such as
receiver processing and analytics system 110 or 110', may be
configured to generate a list of one or more health, fitness,
operation, or performance statuses.
[0346] In one embodiment, historical data, for example, from a
person's medical history, may be used to provide a list of one or
more probable statuses of an individual. For example, by knowing an
individual's historical heart rate, breathing rate, and/or sweat
rate, a combination of sensor derived data may indicate a moderate
level of stress and thus one status determination for a first
individual. Whereas the same combination of sensor derived data may
indicate a high level of stress in a second individual and thus, a
second status determination (e.g., panic attack).
[0347] In one embodiment, the sensor based HFOP models that are
used in status determination may be a function of medical history
data. For example, where medical history data shows one or more
concussions, particular thresholds and/or tolerances related to
sensor derived data may be adjusted such that status determination
of a particular status may be more likely and/or is more
conservative (e.g., erring on the side of caution). For example,
acceptable tolerances in differences in reaction time, eye dilation
data or the like may be lowered in individuals who have suffered
concussions in their past.
[0348] As shown in block 1460 of FIG. 14, an apparatus, such as
receiver processing and analytics system 110 or 110', may be
configured to utilize an action determination and a status
determination to provide a HFOP status determination.
[0349] In one embodiment, HFOP status determination may then be
compared to one or more pre-defined normal, abnormal or unexpected
HFOP statuses. For example, pre-defined abnormal HFOP status may
include pulled muscles, broken bones, concussions, or the like. For
example, multi-dimensional location data from a person's knees
and/or ankles may show a progression of running at top speed,
making a quick turn, and slowing down and/or hopping, where one
probable HFOP status may be pulling a muscle, which is turn may be
identified as an abnormal or unexpected HFOP status.
[0350] As shown in block 1470 of FIG. 14, an apparatus, such as
receiver processing and analytics system 110 or 110', may be
configured for causing an alert in response to an HFOP status
determination of one or more particular HFOP statuses.
[0351] As will be appreciated, any such computer program
instructions and/or other type of code may be loaded onto a
computer, processor or other programmable apparatus's circuitry to
produce a machine, such that the computer, processor other
programmable circuitry that execute the code on the machine create
the means for implementing various functions, including those
described herein.
[0352] As described above and as will be appreciated based on this
disclosure, embodiments of the present invention may be configured
as methods, mobile devices, backend network devices, and the like.
Accordingly, embodiments may comprise various means including
entirely of hardware or any combination of software and hardware.
Furthermore, embodiments may take the form of a computer program
product on at least one non-transitory computer-readable storage
medium having computer-readable program instructions (e.g.,
computer software) embodied in the storage medium. Any suitable
computer-readable storage medium may be utilized including
non-transitory hard disks, CD-ROMs, flash memory, optical storage
devices, or magnetic storage devices.
[0353] Embodiments of the present invention have been described
above with reference to block diagrams and flowchart illustrations
of methods, apparatuses, systems and computer program products. It
will be understood that each block of the circuit diagrams and
process flowcharts, and combinations of blocks in the circuit
diagrams and process flowcharts, respectively, can be implemented
by various means including computer program instructions. These
computer program instructions may be loaded onto a general purpose
computer, special purpose computer, or other programmable data
processing apparatus to produce a machine, such that the computer
program product includes the instructions which execute on the
computer or other programmable data processing apparatus create a
means for implementing the functions specified in the flowchart
block or blocks.
[0354] These computer program instructions may also be stored in a
computer-readable storage device that can direct a computer or
other programmable data processing apparatus to function in a
particular manner, such that the instructions stored in the
computer-readable storage device produce an article of manufacture
including computer-readable instructions for implementing the
function discussed herein. The computer program instructions may
also be loaded onto a computer or other programmable data
processing apparatus to cause a series of operational steps to be
performed on the computer or other programmable apparatus to
produce a computer-implemented process such that the instructions
that execute on the computer or other programmable apparatus
provide steps for implementing the functions discussed herein.
[0355] Accordingly, blocks of the block diagrams and flowchart
illustrations support combinations of means for performing the
specified functions, combinations of steps for performing the
specified functions and program instruction means for performing
the specified functions. It will also be understood that each block
of the circuit diagrams and process flowcharts, and combinations of
blocks in the circuit diagrams and process flowcharts, can be
implemented by special purpose hardware-based computer systems that
perform the specified functions or steps, or combinations of
special purpose hardware and computer instructions.
Computing Device Architecture
[0356] In some embodiments of the present invention, an apparatus,
such as a receiver processing and analytics system 110 or 110', tag
data/sensor data filter 112, HFOP engine 116, individual
dynamics/kinetics engine 120, or HFOP status engine 124 may be
embodied by a computing device. The computing device may include or
be associated with an apparatus 1500 as shown in FIG. 15. In this
regard, the apparatus may include or otherwise be in communication
with a processor 1522, a memory device 1524, a communication
interface 1526 and a user interface 1528. As such, in some
embodiments, although devices or elements are shown as being in
communication with each other, hereinafter such devices or elements
should be considered to be capable of being embodied within the
same device or element and thus, devices or elements shown in
communication should be understood to alternatively be portions of
the same device or element.
[0357] In some embodiments, the processor 1522 (and/or
co-processors or any other processing circuitry assisting or
otherwise associated with the processor) may be in communication
with the memory device 1524 via a bus for passing information among
components of the apparatus. The memory device may include, for
example, one or more volatile and/or non-volatile memories. In
other words, for example, the memory device may be an electronic
storage device (e.g., a computer readable storage medium)
comprising gates configured to store data (e.g., bits) that may be
retrievable by a machine (e.g., a computing device like the
processor). The memory device may be configured to store
information, data, content, applications, instructions, or the like
for enabling the apparatus 1500 to carry out various functions in
accordance with an example embodiment of the present invention. For
example, the memory device could be configured to buffer input data
for processing by the processor. Additionally or alternatively, the
memory device could be configured to store instructions for
execution by the processor.
[0358] As noted above, the apparatus 1500 may be embodied by a
computing device 10 configured to employ an example embodiment of
the present invention. However, in some embodiments, the apparatus
may be embodied as a chip or chip set. In other words, the
apparatus may comprise one or more physical packages (e.g., chips)
including materials, components and/or wires on a structural
assembly (e.g., a baseboard). The structural assembly may provide
physical strength, conservation of size, and/or limitation of
electrical interaction for component circuitry included thereon.
The apparatus may therefore, in some cases, be configured to
implement an embodiment of the present invention on a single chip
or as a single "system on a chip." As such, in some cases, a chip
or chipset may constitute means for performing one or more
operations for providing the functionalities described herein.
[0359] The processor 1522 may be embodied in a number of different
ways. For example, the processor may be embodied as one or more of
various hardware processing means such as a coprocessor, a
microprocessor, a controller, a digital signal processor (DSP), a
processing element with or without an accompanying DSP, or various
other processing circuitry including integrated circuits such as,
for example, an ASIC (application specific integrated circuit), an
FPGA (field programmable gate array), a microcontroller unit (MCU),
a hardware accelerator, a special-purpose computer chip, or the
like. As such, in some embodiments, the processor may include one
or more processing cores configured to perform independently. A
multi-core processor may enable multiprocessing within a single
physical package. Additionally or alternatively, the processor may
include one or more processors configured in tandem via the bus to
enable independent execution of instructions, pipelining and/or
multithreading.
[0360] In an example embodiment, the processor 1522 may be
configured to execute instructions stored in the memory device 1524
or otherwise accessible to the processor. Alternatively or
additionally, the processor may be configured to execute hard coded
functionality. As such, whether configured by hardware or software
methods, or by a combination thereof, the processor may represent
an entity (e.g., physically embodied in circuitry) capable of
performing operations according to an embodiment of the present
invention while configured accordingly. Thus, for example, when the
processor is embodied as an ASIC, FPGA or the like, the processor
may be specifically configured hardware for conducting the
operations described herein. Alternatively, as another example,
when the processor is embodied as an executor of software
instructions, the instructions may specifically configure the
processor to perform the algorithms and/or operations described
herein when the instructions are executed. However, in some cases,
the processor may be a processor of a specific device (e.g., a head
mounted display) configured to employ an embodiment of the present
invention by further configuration of the processor by instructions
for performing the algorithms and/or operations described herein.
The processor may include, among other things, a clock, an
arithmetic logic unit (ALU) and logic gates configured to support
operation of the processor. In one embodiment, the processor may
also include user interface circuitry configured to control at
least some functions of one or more elements of the user interface
1528.
[0361] Meanwhile, the communication interface 1526 may be any means
such as a device or circuitry embodied in either hardware or a
combination of hardware and software that is configured to receive
and/or transmit data between the computing device 10 and a server
12. In this regard, the communication interface 1526 may include,
for example, an antenna (or multiple antennas) and supporting
hardware and/or software for enabling communications wirelessly.
Additionally or alternatively, the communication interface may
include the circuitry for interacting with the antenna(s) to cause
transmission of signals via the antenna(s) or to handle receipt of
signals received via the antenna(s). For example, the
communications interface may be configured to communicate
wirelessly with displays, such as via Wi-Fi, Bluetooth or other
wireless communications techniques. In some instances, the
communication interface may alternatively or also support wired
communication. As such, for example, the communication interface
may include a communication modem and/or other hardware/software
for supporting communication via cable, digital subscriber line
(DSL), universal serial bus (USB) or other mechanisms. For example,
the communication interface may be configured to communicate via
wired communication with other components of the computing
device.
[0362] The user interface 1528 may be in communication with the
processor 1522, such as the user interface circuitry, to receive an
indication of a user input and/or to provide an audible, visual,
mechanical, or other output to a user. As such, the user interface
may include, for example, a keyboard, a mouse, a joystick, a
display, a touch screen display, a microphone, a speaker, and/or
other input/output mechanisms. In some embodiments, a display may
refer to display on a screen, on a wall, on glasses (e.g.,
near-eye-display), in the air, etc. The user interface may also be
in communication with the memory 1524 and/or the communication
interface 1526, such as via a bus.
[0363] Many modifications and other embodiments of the inventions
set forth herein will come to mind to one skilled in the art to
which these embodiments of the invention pertain having the benefit
of the teachings presented in the foregoing descriptions and the
associated drawings. Therefore, it is to be understood that the
embodiments of the invention are not to be limited to the specific
embodiments disclosed and that modifications and other embodiments
are intended to be included within the scope of the appended
claims. Although specific terms are employed herein, they are used
in a generic and descriptive sense only and not for purposes of
limitation.
* * * * *