U.S. patent application number 14/664987 was filed with the patent office on 2016-09-29 for monitoring a person for indications of a brain injury.
The applicant listed for this patent is INTERNATIONAL BUSINESS MACHINES CORPORATION. Invention is credited to JAMES R. KOZLOSKI, MARK C. H. LAMOREY, CLIFFORD A. PICKOVER, JOHN J. RICE.
Application Number | 20160278633 14/664987 |
Document ID | / |
Family ID | 56973842 |
Filed Date | 2016-09-29 |
United States Patent
Application |
20160278633 |
Kind Code |
A1 |
KOZLOSKI; JAMES R. ; et
al. |
September 29, 2016 |
MONITORING A PERSON FOR INDICATIONS OF A BRAIN INJURY
Abstract
Embodiments include methods, systems and computer program
products for monitoring a user of a helmet for a traumatic brain
injury. Aspects include monitoring one or more eyes of the user
with a camera embedded in the helmet and analyzing, by a processor,
one or more characteristics of the one or more eyes of the user.
Aspects also include determining whether the one or more
characteristics of the eyes indicate that the user may have
suffered the traumatic brain injury and creating an alert that the
user of the helmet may have suffered the traumatic brain injury
Inventors: |
KOZLOSKI; JAMES R.; (NEW
FAIRFIELD, CT) ; LAMOREY; MARK C. H.; (WILLISTON,
VT) ; PICKOVER; CLIFFORD A.; (YORKTOWN HEIGHTS,
NY) ; RICE; JOHN J.; (MOHEGAN LAKE, NY) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
INTERNATIONAL BUSINESS MACHINES CORPORATION |
Armonk |
NY |
US |
|
|
Family ID: |
56973842 |
Appl. No.: |
14/664987 |
Filed: |
March 23, 2015 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 3/112 20130101;
A61B 2562/0204 20130101; A61B 5/746 20130101; A61B 3/0008 20130101;
A61B 5/1114 20130101; A61B 5/7278 20130101; A61B 5/7282 20130101;
A61B 3/14 20130101; A61B 5/4064 20130101; A61B 2560/0475 20130101;
A61B 3/113 20130101; A61B 2562/0219 20130101; A61B 5/747 20130101;
A61B 5/6803 20130101; A61B 3/0025 20130101; A61B 3/145
20130101 |
International
Class: |
A61B 3/11 20060101
A61B003/11; A61B 3/14 20060101 A61B003/14; A61B 3/00 20060101
A61B003/00; A61B 5/00 20060101 A61B005/00 |
Claims
1. (canceled)
2. (canceled)
3. (canceled)
4. (canceled)
5. (canceled)
6. (canceled)
7. (canceled)
8. A computer program product for monitoring a user of a helmet for
a traumatic brain injury, the computer program product comprising:
a non-transitory storage medium readable by a processing circuit
and storing instructions for execution by the processing circuit
for performing a method comprising: monitoring one or more eyes of
the user with a camera embedded in the helmet; analyzing, by a
processor, one or more characteristics of the one or more eyes of
the user; determining whether the one or more characteristics of
the eyes indicate that the user may have suffered the traumatic
brain injury; and creating an alert that the user of the helmet may
have suffered the traumatic brain injury.
9. The computer program product of claim 8, wherein the one or more
characteristics of the one or more eyes of the user includes a
dilation of a pupil.
10. The computer program product of claim 8, wherein the analyzing
is performed based on a detected acceleration of the helmet
exceeding a threshold level.
11. The computer program product of claim 8, wherein the
determination that the one or more characteristics of the eyes
indicate that the user may have suffered the traumatic brain injury
is based on a comparison of the one or more characteristics of the
one or more eyes of the user to a baseline reading of the one or
more characteristics of the one or more eyes of the user.
12. The computer program product of claim 8, wherein the
determination that the one or more characteristics of the eyes
indicate that the user may have suffered the traumatic brain injury
is based on a determination that the user has Anisocoria.
13. The computer program product of claim 8, wherein the monitoring
further comprises illuminating the one or more eyes with a light
source in the helmet.
14. The computer program product of claim 8, further comprising
transmitting the alert to a separate computer system for processing
and further analysis.
15. A helmet for monitoring a user of for a traumatic brain injury
comprising: a camera and a processor configured for performing a
method comprising: monitoring one or more eyes of the user with the
camera; analyzing, by the processor, one or more characteristics of
the one or more eyes of the user; determining whether the one or
more characteristics of the eyes indicate that the user may have
suffered the traumatic brain injury; and creating an alert that the
user of the helmet may have suffered the traumatic brain
injury.
16. The helmet of claim 15, wherein the one or more characteristics
of the one or more eyes of the user includes a dilation of a
pupil.
17. The helmet of claim 15, wherein the analyzing is performed
based on a detected acceleration of the helmet exceeding a
threshold level.
18. The helmet of claim 15, wherein the determination that the one
or more characteristics of the eyes indicate that the user may have
suffered the traumatic brain injury is based on a comparison of the
one or more characteristics of the one or more eyes of the user to
a baseline reading of the one or more characteristics of the one or
more eyes of the user.
19. The helmet of claim 15, wherein the determination that the one
or more characteristics of the eyes indicate that the user may have
suffered the traumatic brain injury is based on a determination
that the user has Anisocoria.
20. The helmet of claim 15, wherein the monitoring further
comprises illuminating the one or more eyes with a light source in
the helmet.
Description
BACKGROUND
[0001] The present disclosure relates to monitoring a person for
indications of a brain injury, and more specifically, to methods,
systems and computer program products for using sensors in a
uniform to monitor a person for indications of a brain injury.
[0002] Generally speaking, safety is a primary concern for both
users of helmets and manufacturers of helmets. Helmets are used by
individuals that participate in activities that have risk of head
trauma, such as the area of sports, biking, motorcycling, etc.
While helmets have traditionally been used to provide protection
from blunt force trauma to the head, an increased awareness of
concussion causing forces has motivated a need for advances in
helmet technology to provide increased protection against
concussions. A concussion is a type of traumatic brain injury that
is caused by a blow to the head that shakes the brain inside the
skull due to linear or rotational accelerations. Recently, research
has linked concussions to a range of health problems, from
depression to Alzheimer's, along with a range of brain injuries.
Unlike severe traumatic brain injuries, which result in lesions or
bleeding inside the brain and are detectable using standard medical
imaging, a concussion is often invisible in brain tissue, and
therefore only detectable by means of a cognitive change, where
that change is measurable by changes to brain tissue actions,
either neurophysiological or through muscle actions caused by the
brain and the muscles resulting effects on the environment, for
example, speech sounds.
[0003] Currently available helmets use accelerometers to measure
the forces that the helmet, and therefore the head of the user,
experiences. These accelerometers can be used to indicate when a
force experienced by a helmet may be sufficiently large so as to
pose a risk of a concussion to the user. However, currently
available helmets are prone to providing false positives which can
lead to unnecessary downtime for the user of the helmet. In
addition, a large number of false positives may lead to individuals
disregarding the indications generated and therefore a further
degradation of the effectiveness of the monitoring.
SUMMARY
[0004] In accordance with an embodiment, a method for monitoring a
user of a helmet for a traumatic brain injury includes monitoring
one or more eyes of the user with a camera embedded in the helmet
and analyzing, by a processor, one or more characteristics of the
one or more eyes of the user. The method also includes determining
whether the one or more characteristics of the eyes indicate that
the user may have suffered the traumatic brain injury and creating
an alert that the user of the helmet may have suffered the
traumatic brain injury
[0005] In accordance with another embodiment, a helmet for
monitoring a user of for a traumatic brain injury includes a
processor and a camera. The processor is configured to perform a
method that includes monitoring one or more eyes of the user with
the camera and analyzing, by a processor, one or more
characteristics of the one or more eyes of the user. The method
also includes determining whether the one or more characteristics
of the eyes indicate that the user may have suffered the traumatic
brain injury and creating an alert that the user of the helmet may
have suffered the traumatic brain injury
[0006] In accordance with a further embodiment, a computer program
product for monitoring a user of a helmet for a traumatic brain
injury includes a non-transitory storage medium readable by a
processing circuit and storing instructions for execution by the
processing circuit for performing a method. The method includes
monitoring one or more eyes of the user with a camera embedded in
the helmet and analyzing, by a processor, one or more
characteristics of the one or more eyes of the user. The method
also includes determining whether the one or more characteristics
of the eyes indicate that the user may have suffered the traumatic
brain injury and creating an alert that the user of the helmet may
have suffered the traumatic brain injury
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] The subject matter which is regarded as the invention is
particularly pointed out and distinctly claimed in the claims at
the conclusion of the specification. The forgoing and other
features, and advantages of the invention are apparent from the
following detailed description taken in conjunction with the
accompanying drawings in which:
[0008] FIG. 1 is a block diagram illustrating one example of a
processing system for practice of the teachings herein;
[0009] FIG. 2 is a block diagram illustrating one example of a
uniform in accordance with an exemplary embodiment;
[0010] FIG. 3 is a flow diagram of a method for monitoring a user
for a traumatic brain injury in accordance with an exemplary
embodiment;
[0011] FIG. 4 is a flow diagram of another method for monitoring a
user for a traumatic brain injury in accordance with an exemplary
embodiment;
[0012] FIG. 5 is a flow diagram of a method for monitoring a user
for a traumatic brain injury with a camera embedded in a helmet in
accordance with an exemplary embodiment;
[0013] FIG. 6 is a flow diagram of another method for monitoring a
user for a traumatic brain injury with a microphone embedded in a
helmet in accordance with an exemplary embodiment;
[0014] FIG. 7 is a flow diagram of a method for monitoring a user
for a traumatic brain injury based on a gait of the user in
accordance with an exemplary embodiment;
[0015] FIG. 8 is a flow diagram of another method for monitoring a
user for a traumatic brain injury based on a gait of the user in
accordance with an exemplary embodiment; and
[0016] FIG. 9 is a flow diagram of a further method for monitoring
a user for a traumatic brain injury in accordance with an exemplary
embodiment.
DETAILED DESCRIPTION
[0017] In accordance with exemplary embodiments of the disclosure,
methods, systems and computer program products for using sensors in
a helmet, or another suitable piece of a uniform, to monitor a
wearer for indications of a brain injury are provided. In exemplary
embodiments, the sensors may include one or more of accelerometers,
gyroscopes, cameras, microphones, or the like. In general, the
outputs of the sensors are used to monitor one or more physical
characteristics or actions of the user for signs of a traumatic
brain injury, such as a concussion. In exemplary embodiments, a
combination of senor readings may be used to detect a possible
traumatic brain injury. In exemplary embodiments, once a possible
traumatic brain injury is detected, a signal indicative of the
detected traumatic brain injury may be created and transmitted.
[0018] Referring to FIG. 1, there is shown an embodiment of a
processing system 100 for implementing the teachings herein. In
this embodiment, the system 100 has one or more central processing
units (processors) 101a, 101b, 101c, etc. (collectively or
generically referred to as processor(s) 101). In one embodiment,
each processor 101 may include a reduced instruction set computer
(RISC) microprocessor. Processors 101 are coupled to system memory
114 and various other components via a system bus 113. Read only
memory (ROM) 102 is coupled to the system bus 113 and may include a
basic input/output system (BIOS), which controls certain basic
functions of system 100.
[0019] FIG. 1 further depicts an input/output (I/O) adapter 107 and
a network adapter 106 coupled to the system bus 113. I/O adapter
107 may be a small computer system interface (SCSI) adapter that
communicates with a hard disk 103 and/or tape storage drive 105 or
any other similar component. I/O adapter 107, hard disk 103, and
tape storage device 105 are collectively referred to herein as mass
storage 104. Operating system 120 for execution on the processing
system 100 may be stored in mass storage 104. A network adapter 106
interconnects bus 113 with an outside network 116 enabling data
processing system 100 to communicate with other such systems. A
screen (e.g., a display monitor) 115 is connected to system bus 113
by display adaptor 112, which may include a graphics adapter to
improve the performance of graphics intensive applications and a
video controller. In one embodiment, adapters 107, 106, and 112 may
be connected to one or more I/O busses that are connected to system
bus 113 via an intermediate bus bridge (not shown). Suitable I/O
buses for connecting peripheral devices such as hard disk
controllers, network adapters, and graphics adapters typically
include common protocols, such as the Peripheral Component
Interconnect (PCI). Additional input/output devices are shown as
connected to system bus 113 via user interface adapter 108 and
display adapter 112. A keyboard 109, mouse 110, and speaker 111 all
interconnected to bus 113 via user interface adapter 108, which may
include, for example, a Super I/O chip integrating multiple device
adapters into a single integrated circuit.
[0020] Thus, as configured in FIG. 1, the system 100 includes
processing capability in the form of processors 101, storage
capability including system memory 114 and mass storage 104, input
means such as keyboard 109 and mouse 110, and output capability
including speaker 111 and display 115. In one embodiment, a portion
of system memory 114 and mass storage 104 collectively store an
operating system such as the AIX.RTM. operating system from IBM
Corporation to coordinate the functions of the various components
shown in FIG. 1.
[0021] Referring to FIG. 2, a block diagram illustrating one
example of a uniform 200 in accordance with an exemplary embodiment
is shown. As used herein a uniform is an outfit worn by individual
while participating in an activity. The term uniform may include,
but is not intended to be limited to, a helmet. In exemplary
embodiments, the uniform 200 includes one or more of the following
an accelerometer 202, a camera 204, a microphone 206, a gyroscope
208, a processor 210, a transceiver 212, a power supply 214 and a
memory 216. In exemplary embodiments, the power supply 214 may be a
battery configured to provide power to one or more of the
accelerometer 202, the camera 204, the microphone 206, the
gyroscope 208, the processor 210 and the transceiver 212.
[0022] The processor 210 is configured to receive an output from
one or more of the accelerometer 202, the camera 204, the
microphone 206 and the gyroscope 208 and to determine if a user of
the uniform may have suffered a traumatic brain injury based on the
inputs received. In exemplary embodiments, the processor 210 may
also be used to create a baseline profile of the user based on
input form the accelerometer 202, the camera 204, the microphone
206 and the gyroscope 208 during a first time period and may store
the baseline profile in the memory 216. The processor 210 may
compare the readings from the accelerometer 202, the camera 204,
the microphone 206 and the gyroscope 208 during a second time
period with the stored baseline profile to make determination that
the user of the uniform may have suffered a traumatic brain injury.
Upon making a determination that the user of the uniform may have
suffered a traumatic brain injury the processor 210 utilizes the
transceiver to transmit an alert that the user of the uniform may
have suffered a traumatic brain injury.
[0023] In exemplary embodiments, the processor 210 may be
configured to selectively activate one of the camera 204, the
microphone 206 and the gyroscope 208 based on the output received
from the accelerometer 202 exceeding a threshold level. As will be
appreciated by those of ordinary skill in the art, the number and
placement of the various sensors (accelerometer 202, camera 204,
microphone 206, and gyroscope 208) will depend upon the type of the
uniform and the metrics of the user to be monitored. In exemplary
embodiments, the user metrics that can be monitored include, but
are not limited to, the gait of the user, the speech of the user
and the eyes of the user.
[0024] Referring now to FIG. 3, a flow diagram of a method 300 for
monitoring a user for a traumatic brain injury in accordance with
an exemplary embodiment is shown. As shown at block 302, the method
300 includes monitoring a plurality of sensors in a uniform. In
exemplary embodiments, the plurality of sensors includes an
accelerometer and at least one of a camera, a gyroscope and a
microphone. Next, as shown at block 304, the method 300 includes
analyzing an output of the plurality of sensors for a time period.
In exemplary embodiments, analyzing the output of the plurality of
sensors may include comparing the output of the plurality of
sensors to one or more stored profiles corresponding to normal
measurements from each of the plurality of sensors or to one or
more thresholds for each of the plurality of sensors. As shown at
decision block 306, the method 300 includes determining if the
output of the plurality of sensors indicates a user of the uniform
may have suffered a traumatic brain injury. If the output of the
plurality of sensors indicates that the user of the uniform may
have suffered a traumatic brain injury, the method 300 proceeds to
block 308 and includes transmitting an alert that the user of the
uniform may have suffered a traumatic brain injury. Otherwise, the
method 300 returns to block 302 and continues to monitor the output
of the plurality of sensors.
[0025] Referring now to FIG. 4, a flow diagram of another method
400 for monitoring a user for a traumatic brain injury in
accordance with an exemplary embodiment is shown. As shown at block
402, the method 400 includes monitoring an acceleration experienced
by a helmet. For example, the acceleration experienced by a helmet
may be measured by one or more accelerators embedded within the
helmet. Next, as shown at decision block 404, the method 400
includes determining if the acceleration experienced by the helmet
exceeds a threshold value. If the acceleration experienced by the
helmet does not exceed the threshold value, the method returns to
block 402 and continues monitoring the acceleration experienced by
the helmet. Otherwise, the method 400 proceeds to block 406 and
includes activating one or more supplemental sensors. In exemplary
embodiments, the one or more supplemental sensors may include a
camera, a gyroscope and/or a microphone.
[0026] The method 400 also includes analyzing an output one or more
supplemental sensors for a time period. In exemplary embodiments,
analyzing the output of the supplemental sensors may include
comparing the output of the supplemental sensors to one or more
stored profiles corresponding to normal measurements from each of
the supplemental sensors or to one or more thresholds for each of
the supplemental sensors. As shown at decision block 410, the
method 400 includes determining if the outputs of one of the one or
more supplemental sensors indicate a user of the helmet may have
suffered a traumatic brain injury. If the output of one of the
supplemental sensors indicates that the user of the helmet may have
suffered a traumatic brain injury, the method 400 proceeds to block
412 and includes transmitting an alert that the user of the uniform
may have suffered a traumatic brain injury. Otherwise, the method
400 returns to block 402 and continues to monitor the acceleration
experienced by the helmet.
[0027] It has been shown that the dilation of the pupil of an
individual's eyes correlates with a cognitive category of the user
and that pupil dilation can be indicative of a traumatic brain
injury. Bleeding inside the skull caused by head injury can cause
Anisocoria, which is an unequal pupil size. Accordingly, in
exemplary embodiments, a helmet is provided that includes one or
more cameras that are used to monitor the eyes of the wearer of the
helmet for signs of a traumatic brain injury. For example, the
camera may capture images of the eyes and the processor may analyze
the pupil dilation of the helmet wearer. In some embodiments, the
cameras may include a light source that is configured to project a
light onto the eyes of the wearer of the helmet to aid in the
evaluation of the pupil dilation response.
[0028] In exemplary embodiments, the processor may also utilize one
or more environmental influences, such as an ambient lighting
level, in determining a cognitive state and a risk category for
traumatic brain injury. A plurality of cognitive states and risk
categories can be created by monitoring the wearer of the helmet
over time and during different states, i.e., active, inactive,
playing, resting, etc. These cognitive states and risk categories
can be stored and compared with real-time data collected by camera
to map the user's current state to a known cognitive state and risk
category. Deviations from expected results during known states can
be used detect when the risk of traumatic brain injury is
increased.
[0029] Referring now to FIG. 5, a flow diagram of a method 500 for
monitoring a user for a traumatic brain injury with a camera
embedded in a helmet in accordance with an exemplary embodiment is
shown. As shown at block 502, the method 500 includes monitoring
eyes of a user of the helmet with the camera embedded in the
helmet. Next, as shown at block 504, the method 500 includes
analyzing one or more characteristics of the eyes of the user. In
exemplary embodiments, the one or more characteristics of the eyes
of the user may include, but are not limited to, the pupil size of
the eyes and any differences in the size of one pupil verses the
other. Next, as shown at decision block 506, the method 500
includes determining if the one or more characteristics of the eyes
indicate that the user may have suffered a traumatic brain injury.
If the one or more characteristics of the eyes indicate that the
user may have suffered a traumatic brain injury, the method 500
proceeds to block 508 and transmits an alert that the user of the
helmet may have suffered a traumatic brain injury. Otherwise, the
method returns to block 502 and continues to monitor the eyes of
the user of the helmet.
[0030] In exemplary embodiments, the helmet may be configured to
transmit images captured by the camera to a separate computer
system for processing and further analysis. Such transmission may
be periodic, it may be triggered by a threshold image analysis by
the processor embedded in the helmet, or it may be triggered by a
reading from one or more sensors in the helmet, such as the
accelerometer. In exemplary embodiments, the user's history of
collision or medical concerns may also be used to determine a risk
assessment, either by the embedded processor or the separate
processing system. In addition, the helmet may be configured to
provide a real-time feed of the user's cognitive state to increase
the confidence level of the need for a particular alert or
indication. In exemplary embodiments, an aggregate indication may
be used to summarize an overall state of a group of players. This
may also help to potentially identify area of risk in the dynamics
of player-player interaction, overly aggressive players, playing
field conditions, etc.
[0031] As mentioned, changes in pupil dilation can indicate a
severe traumatic brain injury, including internal brain bleeding.
Analyzing the characteristics of the eyes of the user can also
include analytics of pupil dilation that subtle, and may include
changes in pupil diameter and asymmetries in pupil dilation that
are imperceptible to a human observer. These may then be useful in
detecting milder traumatic brain injury such as concussion, which
is typically invisible in medical imaging scans, and is measured
instead a cognitive change. Furthermore, the use of the camera for
pupil tracking can provide measures of patterns saccadic eye
movements, including microsaccades, for the purpose of detecting a
traumatic brain injury.
[0032] It has been shown that an analysis of an individual's speech
is sufficient to assign the individual to a particular cognitive,
psychological, or psychiatric category. Accordingly, in exemplary
embodiments, a helmet is provided that includes one or more
microphones and a processor that are used to monitor the speech of
a user of the helmet for signs of a traumatic brain injury. For
example, the microphones are used to capture the speech of the user
of the helmet and the processor performs analysis on the collected
speech. In one embodiment, the analysis can include a graphical
analysis of words spoken by the user to determine cognitive state,
such as by means of a speech graph. In another embodiment, the
analysis can include an analysis of the prosody of the user's
speech to determine the user's cognitive state and changes thereto.
In many cases, a lack of prosody in the user's speech may be
indicative that the user has suffered a concussion. In other
embodiments, the analysis may include the detection of specific
changes in the individual's speech patterns such as syllable
durations and reduced peak velocity/amplitude ratios.
[0033] In exemplary embodiments, a plurality of cognitive states
and risk categories can be created by monitoring the speech of the
wearer of the helmet over time and during different states, i.e.,
active, inactive, playing, resting, etc. These cognitive states and
risk categories can be stored and compared with the real-time
speech data collected by microphone to map the user's current state
to a known cognitive state and risk category. As a result,
deviations of the current state from one of the known states can be
used detect when the risk of traumatic brain injury is
increased.
[0034] In exemplary embodiments, the helmet may also include a
vibration detection sensor that is disposed close to skull of the
wearer that can be used by the processor to disambiguate speech
generated by the wearer of the helmet from the speech of other
nearby individuals. In exemplary embodiments, the processor
embedded in the helmet may be configured to perform a speech to
text conversion. In other embodiments, the sound captured by the
microphones may be transmitted to a separate system for processing
and further analysis.
[0035] In exemplary embodiments, the user's history of collision or
medical concerns may also be used in determining a risk assessment,
either by the embedded processor or the separate processing system.
In addition, the helmet may be configured to provide a real-time
feed of the speech or the user's cognitive state to increase the
confidence level of the need for a particular alert or indication.
In exemplary embodiments, an aggregate indication may be used to
summarize an overall state of a group of players. This may also
help to potentially identify area of risk in the dynamics of
player-player interaction, overly aggressive players, playing field
conditions, etc.
[0036] Referring now to FIG. 6, a flow diagram of a method 600 for
monitoring a user for a traumatic brain injury with a microphone
embedded in a helmet in accordance with an exemplary embodiment is
shown. As shown at block 602, the method 600 includes monitoring
the speech of a user of the helmet with the microphone embedded in
the helmet. Next, as shown at block 604, the method 600 includes
analyzing one or more characteristics of the speech of the user. In
exemplary embodiments, the one or more characteristics of the
speech of the user may include, but are not limited to, words
spoken by the user (via a speech graph), the prosody of the users
speech, or the rate of the users speech (words/min). Next, as shown
at decision block 606, the method 600 includes determining if the
one or more characteristics of the speech of the user indicate that
the user may have suffered a traumatic brain injury. If the one or
more characteristics of the speech of the user indicate that the
user may have suffered a traumatic brain injury, the method 600
proceeds to block 608 and transmits an alert that the user of the
helmet may have suffered a traumatic brain injury. Otherwise, the
method 600 returns to block 602 and continues to monitor the speech
of the user of the helmet.
[0037] For speech traits and other cognitively correlated traits
described herein, comparing a current cognitive trait to those
measured previously may occur either within an individual, or
across individuals. For example, the category of speech features
that present after concussion may be common across individuals,
even if speech features vary across individuals before concussion.
In this way, detection may be simplified and not require historical
data from the individual, but instead only a set of predetermined
features of a concussed cohort of individuals.
[0038] It has been shown that an analysis of an individual's gait
is sufficient to assign the individual to a particular cognitive
category. In addition, quantitative gait analysis can be used to
identify walking abnormalities, which can be used as an indicator
that an individual may have suffered a traumatic brain injury.
Accordingly, in exemplary embodiments, a uniform is provided that
includes one or more sensors that are used to monitor the gait of a
user for signs of a traumatic brain injury. For example, the
uniform may include multiple accelerometers disposed in different
locations of the uniform and a processor to analyze the data
collected by the accelerometers. In one embodiment, the uniform may
include accelerometers disposed on parts of the body to measures
posture and stride and accelerometers disposed in a helmet to
monitor gait. In exemplary embodiments, the system creates and
stores a baseline profile of the user based on input form the
accelerometers and compares the real-time readings from the
accelerometers to make determination that the user of the uniform
may have suffered a traumatic brain injury.
[0039] Referring now to FIG. 7, a flow diagram of a method 700 for
monitoring a user for a traumatic brain injury based on a gait of
the user in accordance with an exemplary embodiment is shown. As
shown at block 702, the method 700 includes monitoring a gait of a
user with one or more accelerometers disposed on the user. Next, as
shown at block 704, the method 700 includes analyzing one or more
characteristics of the gait of the user. In exemplary embodiments,
the one or more characteristics of the gait of the user may
include, but are not limited to, a duty factor and a
forelimb-hindlimb (arm-leg) phase relationship. The duty factor is
the percent of a total cycle which a given foot is on the ground
and a forelimb-hindlimb phase is the temporal relationship between
the limb pairs. Next, as shown at decision block 706, the method
600 includes determining if the one or more characteristics of the
gait of the user indicate that the user may have suffered a
traumatic brain injury. If the one or more characteristics of the
gait of the user indicate that the user may have suffered a
traumatic brain injury, the method 700 proceeds to block 708 and
transmits an alert that the user of the helmet may have suffered a
traumatic brain injury. Otherwise, the method 700 returns to block
702 and continues to monitor the gait of the user of the
helmet.
[0040] In one embodiment, a system is provided that analyzes
acceleration data received from one or more accelerometer in a
uniform to determine the postural and movement indicators of a
user. In exemplary embodiments, the postural and movement
indicators are correlated to one of a group of play categories. In
general, muscle memory would dictate that an individual would only
experience slight deviations of acceleration during certain
categories of play. Accordingly, the system can determine the state
of play and then compare the expected acceleration data for that
state of play to the observed readings. A brain or somatic injury,
for example to the cerebellum, may indicated by when measures fall
outside this expected range given a play category. In exemplary
embodiments, the categories, or states of play, can be shared with
a separate analytics processor to determine risk of certain brain
injuries, given the category of play.
[0041] Referring now to FIG. 8, a flow diagram of a method 800 for
monitoring a user for a traumatic brain injury based on a gait of
the user in accordance with an exemplary embodiment is shown. As
shown at block 802, the method 800 includes monitoring a gait of a
user. In exemplary embodiments, the gait may be monitored by one or
more accelerometers disposed on the user and/or by video system
that monitors the movement of the user. Next, as shown at block
804, the method 800 includes analyzing the gait of the user to
determine the postural and movement indicators of a user. The
method 800 also includes determining a state of play of the user
based on the postural and movement indicators of the user, as shown
at block 806. Next, as shown at block 808, the method 800 includes
comparing the gate of the user with an expected gait for the state
of play. Next, as shown at decision block 810, the method 800
includes determining if the comparison indicates that the user may
have suffered a traumatic brain injury. If the comparison indicates
that the user may have suffered a traumatic brain injury, the
method 800 proceeds to block 812 and creates an alert that the user
of the helmet may have suffered a traumatic brain injury.
Otherwise, the method 800 returns to block 802 and continues to
monitor the gait of the user.
[0042] Referring now to FIG. 9, a flow diagram of a method 900 for
monitoring a user for a traumatic brain injury in accordance with
an exemplary embodiment is shown. As shown at block 902, the method
900 includes monitoring a user during a first time period with one
or more sensors. Next, as shown at block 904, the method 900
includes creating a baseline user profile based on an output from
the one or more sensors during the first time period. The method
900 also includes monitoring the user during a second time period
with the one or more sensors, as shown at block 906. Next, as shown
at block 908, the method 900 includes comparing the output from the
one or more sensors during the second time period with the baseline
user profile. Next, as shown at decision block 910, the method 900
includes determining if the comparison indicates that the user may
have suffered a traumatic brain injury. If the comparison indicates
that the user may have suffered a traumatic brain injury, the
method 900 proceeds to block 812 and creates an alert that the user
of the helmet may have suffered a traumatic brain injury.
Otherwise, the method 900 returns to block 906 and continues to
monitor the user.
[0043] Technical effects and benefits include uniforms that are
configured to monitor individuals for signs of a traumatic brain
injury based on a variety of factors. In addition, the uniforms can
be configured to provide an indication that a user has suffered a
traumatic brain injury with an increased confidence by monitoring
multiple different characteristics of the user. Such uniforms and
helmet can be utilized by soldiers, athletes, and other individuals
at risk for traumatic brain injuries.
[0044] Analysis of measurements across sensors may make use of
Bayes' rule. The confidence determining step 910 determines a
confidence level indicating a likelihood of a user to have a
traumatic brain injury. For example, a confidence determining
module may determine that a user has had a concussion. In some
embodiments, the confidence determining module uses Bayesian
inference to compute the conditional probability
P(concussion|evidence), where evidence includes any measure of the
user. P(concussion|evidence) indicates the probability of
concussion given that evidence is true, meaning it indicates the
probability or level of brain injury that the user has had, given
that the user has produced certain measures. If the probability is
sufficiently high (i.e., above a threshold probability), the
confidence determining module determines that it is very likely the
user has had a concussion.
[0045] The conditional probability P(concussion|evidence), which is
the probability of concussion given that evidence is true, may be
computed using the equation (1) according to Bayes rule:
P ( concussion | evidence ) = P ( evidence | concussion ) .times. P
( concussion ) P ( evidence ) ( 1 ) ##EQU00001##
[0046] where P(concussion) and P(evidence) are the probabilities of
concussion and evidence, respectively, and P(evidence|concussion)
is the probability of evidence given that concussion is true. In
some embodiments, P(evidence|concussion) is computed using a
probabilistic model that relates concussion (i.e., the condition)
to evidence (i.e., the measures), and this model is learned in
advance using, e.g., Naive Bayes, or Bayesian Network models. In
the absence of any other evidence (i.e., using only the
expressions), P(concussion) is a uniform distribution--there is a
certain probability that the user has a concussion and a certain
probability that the user does not have a concussion before the
measurement begins. P(concussion), however, may not be a uniform
distribution if evidence other than the expressions of the user is
considered before the conversation begins. For instance,
P(concussion) may start from a 30% probability that the user is
concussed. In such cases, the number of measures needed for
P(concussion|evidence) to exceed the threshold probability may be
fewer.
[0047] The present invention may be a system, a method, and/or a
computer program product. The computer program product may include
a computer readable storage medium (or media) having computer
readable program instructions thereon for causing a processor to
carry out aspects of the present invention.
[0048] The computer readable storage medium can be a tangible
device that can retain and store instructions for use by an
instruction execution device. The computer readable storage medium
may be, for example, but is not limited to, an electronic storage
device, a magnetic storage device, an optical storage device, an
electromagnetic storage device, a semiconductor storage device, or
any suitable combination of the foregoing. A non-exhaustive list of
more specific examples of the computer readable storage medium
includes the following: a portable computer diskette, a hard disk,
a random access memory (RAM), a read-only memory (ROM), an erasable
programmable read-only memory (EPROM or Flash memory), a static
random access memory (SRAM), a portable compact disc read-only
memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a
floppy disk, a mechanically encoded device such as punch-cards or
raised structures in a groove having instructions recorded thereon,
and any suitable combination of the foregoing. A computer readable
storage medium, as used herein, is not to be construed as being
transitory signals per se, such as radio waves or other freely
propagating electromagnetic waves, electromagnetic waves
propagating through a waveguide or other transmission media (e.g.,
light pulses passing through a fiber-optic cable), or electrical
signals transmitted through a wire.
[0049] Computer readable program instructions described herein can
be downloaded to respective computing/processing devices from a
computer readable storage medium or to an external computer or
external storage device via a network, for example, the Internet, a
local area network, a wide area network and/or a wireless network.
The network may comprise copper transmission cables, optical
transmission fibers, wireless transmission, routers, firewalls,
switches, gateway computers and/or edge servers. A network adapter
card or network interface in each computing/processing device
receives computer readable program instructions from the network
and forwards the computer readable program instructions for storage
in a computer readable storage medium within the respective
computing/processing device.
[0050] Computer readable program instructions for carrying out
operations of the present invention may be assembler instructions,
instruction-set-architecture (ISA) instructions, machine
instructions, machine dependent instructions, microcode, firmware
instructions, state-setting data, or either source code or object
code written in any combination of one or more programming
languages, including an object oriented programming language such
as Smalltalk, C++ or the like, and conventional procedural
programming languages, such as the "C" programming language or
similar programming languages. The computer readable program
instructions may execute entirely on the user's computer, partly on
the user's computer, as a stand-alone software package, partly on
the user's computer and partly on a remote computer or entirely on
the remote computer or server. In the latter scenario, the remote
computer may be connected to the user's computer through any type
of network, including a local area network (LAN) or a wide area
network (WAN), or the connection may be made to an external
computer (for example, through the Internet using an Internet
Service Provider). In some embodiments, electronic circuitry
including, for example, programmable logic circuitry,
field-programmable gate arrays (FPGA), or programmable logic arrays
(PLA) may execute the computer readable program instructions by
utilizing state information of the computer readable program
instructions to personalize the electronic circuitry, in order to
perform aspects of the present invention.
[0051] Aspects of the present invention are described herein with
reference to flowchart illustrations and/or block diagrams of
methods, apparatus (systems), and computer program products
according to embodiments of the invention. It will be understood
that each block of the flowchart illustrations and/or block
diagrams, and combinations of blocks in the flowchart illustrations
and/or block diagrams, can be implemented by computer readable
program instructions.
[0052] These computer readable program instructions may be provided
to a processor of a general purpose computer, special purpose
computer, or other programmable data processing apparatus to
produce a machine, such that the instructions, which execute via
the processor of the computer or other programmable data processing
apparatus, create means for implementing the functions/acts
specified in the flowchart and/or block diagram block or blocks.
These computer readable program instructions may also be stored in
a computer readable storage medium that can direct a computer, a
programmable data processing apparatus, and/or other devices to
function in a particular manner, such that the computer readable
storage medium having instructions stored therein comprises an
article of manufacture including instructions which implement
aspects of the function/act specified in the flowchart and/or block
diagram block or blocks.
[0053] The computer readable program instructions may also be
loaded onto a computer, other programmable data processing
apparatus, or other device to cause a series of operational steps
to be performed on the computer, other programmable apparatus or
other device to produce a computer implemented process, such that
the instructions which execute on the computer, other programmable
apparatus, or other device implement the functions/acts specified
in the flowchart and/or block diagram block or blocks.
[0054] The flowchart and block diagrams in the Figures illustrate
the architecture, functionality, and operation of possible
implementations of systems, methods, and computer program products
according to various embodiments of the present invention. In this
regard, each block in the flowchart or block diagrams may represent
a module, segment, or portion of instructions, which comprises one
or more executable instructions for implementing the specified
logical function(s). In some alternative implementations, the
functions noted in the block may occur out of the order noted in
the figures. For example, two blocks shown in succession may, in
fact, be executed substantially concurrently, or the blocks may
sometimes be executed in the reverse order, depending upon the
functionality involved. It will also be noted that each block of
the block diagrams and/or flowchart illustration, and combinations
of blocks in the block diagrams and/or flowchart illustration, can
be implemented by special purpose hardware-based systems that
perform the specified functions or acts or carry out combinations
of special purpose hardware and computer instructions.
* * * * *