U.S. patent application number 14/291992 was filed with the patent office on 2014-12-04 for dynamic sampling.
This patent application is currently assigned to NIKE, Inc.. The applicant listed for this patent is NIKE, Inc.. Invention is credited to Kate Cummings, Manan Goel.
Application Number | 20140358472 14/291992 |
Document ID | / |
Family ID | 51134290 |
Filed Date | 2014-12-04 |
United States Patent
Application |
20140358472 |
Kind Code |
A1 |
Goel; Manan ; et
al. |
December 4, 2014 |
DYNAMIC SAMPLING
Abstract
A wrist-worn athletic performance monitoring system, including
an analysis processor, configured to execute an activity
recognition processes to recognize a sport or activity being
performed by an athlete, and a sampling rate processor, configured
to determine a sampling rate at which an analysis processor is to
sample data from an accelerometer. The sampling rate processor may
determine the sampling rate such that the analysis processor uses a
low amount of electrical energy while still being able to carry out
an activity classification process to classify an activity being
performed.
Inventors: |
Goel; Manan; (Beaverton,
OR) ; Cummings; Kate; (Seattle, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
NIKE, Inc. |
Beaverton |
OR |
US |
|
|
Assignee: |
NIKE, Inc.
Beaverton
OR
|
Family ID: |
51134290 |
Appl. No.: |
14/291992 |
Filed: |
May 30, 2014 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61829814 |
May 31, 2013 |
|
|
|
Current U.S.
Class: |
702/141 ;
702/189 |
Current CPC
Class: |
A61B 5/6831 20130101;
G01D 21/00 20130101; G06F 17/40 20130101; A61B 5/1118 20130101;
G01P 15/00 20130101; G06F 3/05 20130101; A61B 2560/0209 20130101;
G01P 1/00 20130101; A61B 2562/0219 20130101; A61B 5/0002 20130101;
A61B 5/6807 20130101; G06F 19/00 20130101 |
Class at
Publication: |
702/141 ;
702/189 |
International
Class: |
G01P 15/00 20060101
G01P015/00; G01P 1/00 20060101 G01P001/00 |
Claims
1. A unitary apparatus configured to be worn by a user, comprising:
a power supply; a sensor configured to capture acceleration data
from the appendage of the user; an analysis processor; a sampling
rate processor; and a non-transitory computer-readable medium
comprising computer-executable instructions that when executed by
the analysis processor perform at least: receive acceleration data
captured by the sensor; determine a first sampling rate of the
captured sensor data, wherein the sampling rate processor is
configured to select the first sampling rate with a value below an
upper sampling rate in order to reduce power consumption by the
analysis processor from the power supply during sampling; sampling
the data captured by the sensor at the determined first sampling
rate; and classifying the acceleration data into an activity
category selected from a plurality of activity categories being
performed by the user.
2. The unitary apparatus of claim 1, wherein the sampling rate
processor is further configured to: compare a value of the
acceleration data to a threshold value; and determine the first
sampling rate as corresponding to the threshold value.
3. The unitary apparatus of claim 2, wherein the sampling rate
processor determines the first sampling rate corresponding to the
threshold value when the value of the acceleration data is equal to
the threshold value.
4. The unitary apparatus of claim 2, wherein the sampling rate
processor determines the first sampling rate corresponding to the
threshold value when the value of the acceleration data is
numerically closer to, and greater than, a second threshold
value.
5. The unitary apparatus of claim 2, wherein the sampling rate
processor determines the first sampling rate corresponding to the
threshold value when the value of the acceleration data is within a
range of the threshold value.
6. The unitary apparatus of claim 2, wherein the sampling rate
processor determines the first sampling rate as a low-battery
sampling rate corresponding to a low level of stored electrical
energy in the power supply.
7. The unitary apparatus of claim 2, wherein the value of the
acceleration data is an amplitude.
8. The unitary apparatus of claim 2, wherein the value of the
acceleration data is a frequency.
9. The unitary apparatus of claim 1, wherein the analysis processor
is further configured to store sampled acceleration data
corresponding to the classified activity category in the
non-transitory computer-readable medium.
10. The unitary apparatus of claim 1, wherein the sampling rate
processor is further configured to: determine a second sampling
rate corresponding to the activity category into which the
acceleration data is classified, and in response to the determined
second sampling rate, storing, by the analysis processor,
acceleration data sampled at the second sampling rate.
11. The unitary apparatus of claim 10, wherein the second sampling
rate corresponds to a low power consumption rate by the analysis
processor, while maintaining a sampling resolution to capture data
for the classified activity category.
12. The unitary apparatus of claim 1, further comprising: a filter,
for selectively filtering out a signal from the captured
acceleration data.
13. The unitary apparatus of claim 1, wherein the sampling rate
processor receives the captured acceleration data into a memory
register circuit.
14. The unitary apparatus of claim 1, wherein the sampling rate
processor is further configured to: select, in response to the
classification of the acceleration data into an activity category,
a second sensor from which to capture data about the activity of
the user.
15. The unitary apparatus of claim 1, wherein the sampling rate
processor is further configured to: select, in response to receipt
of the captured acceleration data, a second sensor from which to
capture data about the activity of the user.
16. The unitary apparatus of claim 1, further comprising: a
transceiver, for communicating the sampled data to a portable
computer system.
17. The unitary apparatus of claim 1, wherein the first sampling
rate ranges from 0 Hz to 50 Hz.
18. A computer-implemented method for reducing power consumption by
a sensor apparatus, comprising: capturing, by a sensor located on a
device configured to be worn on an appendage of a user,
acceleration data for the wrist of a user; receiving, by a sampling
rate processor of the device, the captured acceleration data;
determining, by the sampling rate processor, a first sampling rate
for the sensor, by selecting the first sampling rate that is below
an upper sampling rate in order to reduce power consumption by an
analysis processor during sampling; sampling, by the analysis
processor, data captured by the sensor at the first sampling rate;
and analyzing, by the analysis processor, the sampled data in an
attempt to classify the acceleration data into an activity category
being performed by the user.
19. The method according to claim 18, further comprising:
comparing, by the sampling rate processor, a value of the
acceleration data to a threshold value; and determining, by the
sampling rate processor, the first sampling rate as corresponding
to the threshold value.
20. A unitary apparatus configured to be worn on an appendage of a
user, comprising: a sensor configured to capture data related to an
activity being carried out by the user; an analysis processor; a
sampling rate processor, configured to: receive the captured data;
determine a first sampling rate, wherein the sampling rate
processor attempts to choose the first sampling rate below an upper
sampling rate in order to reduce power consumption by the analysis
processor during sampling; a non-transitory computer-readable
medium comprising computer-executable instructions that when
executed by the analysis processor perform at least: sampling the
data captured by the sensor at the first sampling rate; and
analyzing the sampled data in an attempt to classify the data into
an activity category being performed by the user.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of, and priority to,
U.S. Provisional Patent Application No. 61/829,814, entitled
"DYNAMIC SAMPLING" filed May 31, 2013. The content of which is
expressly incorporated herein by reference in its entirety for any
and all non-limiting purposes.
BACKGROUND
[0002] Modern technology has given rise to a wide variety of
different electronic and/or communication devices that keep users
in touch with one another, entertained, and informed. A wide
variety of portable electronic devices are available for these
purposes, such as: cellular telephones; personal digital assistants
("PDAs"); pagers; beepers; MP3 or other audio playback devices;
radios; portable televisions, DVD players, or other video playing
devices; watches; GPS systems; etc. Many people like to carry one
or more of these types of devices with them when they exercise
and/or participate in athletic events, for example, to keep them in
contact with others (e.g., in case of inclement weather, injuries;
or emergencies; to contact coaches or trainers; etc.), to keep them
entertained, to provide information (time, direction, location, and
the like).
[0003] Athletic performance monitoring systems also have benefited
from recent advancements in electronic device and digital
technology. Electronic performance monitoring devices allow for
monitoring of many physical or physiological characteristics
associated with exercise or other athletic performances, including,
for example: speed and distance data, altitude data, GPS data,
heart rate, pulse rate, blood pressure data, body temperature, etc.
Specifically, these athletic performance monitoring systems have
benefited from recent advancements in microprocessor design,
allowing increasingly complex computations and processes to be
executed by microprocessors of successively diminutive size. These
modern microprocessors may be used for execution of activity
recognition processes, such that a sport or activity that is being
carried out by an athlete can be recognized, and information
related to that sport or activity can be analyzed and/or stored.
However, these systems are often powered by limited power sources,
such as rechargeable batteries, such that a device may be worn by
an athlete to allow for portable activity monitoring and
recognition. As the computations carried out by athletic
performance monitoring systems have become increasingly complex,
the power consumption of the integral microprocessors carrying out
the computations has increased significantly. Consequently, the
usable time between battery recharges has decreased. Accordingly,
there is a need for more efficient systems and methods for
prolonging the battery life of athletic performance monitoring
systems. Further, certain systems are not configured to permit the
accurate capture of intense fitness activity.
[0004] Aspects of this disclosure are directed towards novel
systems and methods that address one or more of these deficiencies.
Further aspects relate to minimizing other shortcomings in the
art
SUMMARY
[0005] The following presents a simplified summary of the present
disclosure in order to provide a basic understanding of some
aspects of the invention. This summary is not an extensive overview
of the invention. It is not intended to identify key or critical
elements of the invention or to delineate the scope of the
invention. The following summary merely presents some concepts of
the invention in a simplified form as a prelude to the more
detailed description provided below.
[0006] Aspects of the systems and methods described herein relate
non-transitory computer-readable media with computer-executable
instructions for receiving user movement data into a sampling rate
processor on a sensor device. The movement data may be received
from an accelerometer on the device, wherein the device is
positioned on an appendage of a user, and is sampling from the
accelerometer at a first sampling rate. Further, the received
acceleration data may be classified into an activity category that
represents an activity being performed by the user, and based on
this classification, a second sampling rate may be selected for
receiving data from the accelerometer or other sensor(s).
[0007] In another aspect, this disclosure relates to an apparatus
configured to be worn on an appendage of a user, including a power
supply, and a sensor configured to capture data (such as for
example acceleration data) based on the user's movement. The
apparatus may further include an analysis processor, and a sampling
rate processor. In one embodiment, the sampling rate processor
determines a first sampling rate to sample the acceleration data
such that power consumption by the analysis processor is reduced.
The apparatus may further attempt to classify the data sampled at
the first sampling rate into an activity category.
[0008] In yet another aspect, this disclosure relates to
non-transitory computer-readable media with computer-executable
instructions that when executed by a processor is configured to
receive data from a sensor (such as for example, acceleration data
from an accelerometer), identify or select an activity from the
data, and adjust the sampling rate of the sensor based on the
identified activity. Further sampling rates, such as for other
sensors, may be adjusted based upon the identified activity and/or
movements of the user.
[0009] This Summary is provided to introduce a selection of
concepts in a simplified form that are further described below in
the Detailed Description. The Summary is not intended to identify
key features or essential features of the claimed subject matter,
nor is it intended to be used to limit the scope of the claimed
subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0010] FIG. 1 illustrates an example system that may be configured
to provide personal training and/or obtain data from the physical
movements of a user in accordance with example embodiments;
[0011] FIG. 2 illustrates an example computer device that may be
part of or in communication with the system of FIG. 1.
[0012] FIG. 3 shows an illustrative sensor assembly that may be
worn by a user in accordance with example embodiments;
[0013] FIG. 4 shows another example sensor assembly that may be
worn by a user in accordance with example embodiments;
[0014] FIG. 5 shows illustrative locations for sensory input which
may include physical sensors located on/in a user's clothing and/or
be based upon identification of relationships between two moving
body parts of the user;
[0015] FIG. 6 is a schematic block diagram of an exemplary sensor
device 600 that may be utilized in the dynamic adjustment of
sampling rates;
[0016] FIG. 7 is a schematic block diagram depicting one
implementation of a sampling rate processor, such as the sampling
rate processor shown in FIG. 6.
[0017] FIG. 8 is a flowchart diagram of an analysis activation
process in accordance with one embodiment;
[0018] FIG. 9 is a flowchart diagram of a process that may be
utilized to adjust one or more sampling rates in accordance with
one embodiment;
[0019] FIG. 10 is a flowchart diagram of a process that may be
utilized to adjust sampling rates in response to activity
recognition in accordance with one embodiment; and
[0020] FIG. 11 is a flowchart diagram of a process which may be
executed by a sensor device, such as the sensor device of FIG. 6,
in accordance with one embodiment.
DETAILED DESCRIPTION
[0021] Aspects of this disclosure involve obtaining, storing,
and/or processing athletic data relating to the physical movements
of an athlete. The athletic data may be actively or passively
sensed and/or stored in one or more non-transitory storage mediums.
Still further aspects relate to using athletic data to generate an
output, such as for example, calculated athletic attributes,
feedback signals to provide guidance, and/or other information.
These, and other aspects, will be discussed in the context of the
following illustrative examples of a personal training system.
[0022] In the following description of the various embodiments,
reference is made to the accompanying drawings, which form a part
hereof, and in which is shown by way of illustration various
embodiments in which aspects of the disclosure may be practiced. It
is to be understood that other embodiments may be utilized and
structural and functional modifications may be made without
departing from the scope and spirit of the present disclosure.
Further, headings within this disclosure should not be considered
as limiting aspects of the disclosure and the example embodiments
are not limited to the example headings.
I. Example Personal Training System
[0023] A. Illustrative Networks
[0024] Aspects of this disclosure relate to systems and methods
that may be utilized across a plurality of networks. In this
regard, certain embodiments may be configured to adapt to dynamic
network environments. Further embodiments may be operable in
differing discrete network environments. FIG. 1 illustrates an
example of a personal training system 100 in accordance with
example embodiments. Example system 100 may include one or more
interconnected networks, such as the illustrative body area network
(BAN) 102, local area network (LAN) 104, and wide area network
(WAN) 106. As shown in FIG. 1 (and described throughout this
disclosure), one or more networks (e.g., BAN 102, LAN 104, and/or
WAN 106), may overlap or otherwise be inclusive of each other.
Those skilled in the art will appreciate that the illustrative
networks 102-106 are logical networks that may each comprise one or
more different communication protocols and/or network architectures
and yet may be configured to have gateways to each other or other
networks. For example, each of BAN 102, LAN 104 and/or WAN 106 may
be operatively connected to the same physical network architecture,
such as cellular network architecture 108 and/or WAN architecture
110. For example, portable electronic device 112, which may be
considered a component of both BAN 102 and LAN 104, may comprise a
network adapter or network interface card (NIC) configured to
translate data and control signals into and from network messages
according to one or more communication protocols, such as the
Transmission Control Protocol (TCP), the Internet Protocol (IP),
and the User Datagram Protocol (UDP) through one or more of
architectures 108 and/or 110. These protocols are well known in the
art, and thus will not be discussed here in more detail.
[0025] Network architectures 108 and 110 may include one or more
information distribution network(s), of any type(s) or topology(s),
alone or in combination(s), such as for example, cable, fiber,
satellite, telephone, cellular, wireless, etc. and as such, may be
variously configured such as having one or more wired or wireless
communication channels (including but not limited to: WiFi.RTM.,
Bluetooth.RTM., Near-Field Communication (NFC) and/or ANT
technologies). Thus, any device within a network of FIG. 1, (such
as portable electronic device 112 or any other device described
herein) may be considered inclusive to one or more of the different
logical networks 102-106. With the foregoing in mind, example
components of an illustrative BAN and LAN (which may be coupled to
WAN 106) will be described.
[0026] 1. Example Local Area Network
[0027] LAN 104 may include one or more electronic devices, such as
for example, computer device 114. Computer device 114, or any other
component of system 100, may comprise a mobile terminal, such as a
telephone, music player, tablet, netbook or any portable device. In
other embodiments, computer device 114 may comprise a media player
or recorder, desktop computer, server(s), a gaming console, such as
for example, a Microsoft.RTM. XBOX, Sony.RTM. Playstation, and/or a
Nintendo.RTM. Wii gaming consoles. Those skilled in the art will
appreciate that these are merely example devices for descriptive
purposes and this disclosure is not limited to any console or
computing device.
[0028] Those skilled in the art will appreciate that the design and
structure of computer device 114 may vary depending on several
factors, such as its intended purpose. One example implementation
of computer device 114 is provided in FIG. 2, which illustrates a
block diagram of computing device 200. Those skilled in the art
will appreciate that the disclosure of FIG. 2 may be applicable to
any device disclosed herein. Device 200 may include one or more
processors, such as processor 202-1 and 202-2 (generally referred
to herein as "processors 202" or "processor 202"). Processors 202
may communicate with each other or other components via an
interconnection network or bus 204. Processor 202 may include one
or more processing cores, such as cores 206-1 and 206-2 (referred
to herein as "cores 206" or more generally as "core 206"), which
may be implemented on a single integrated circuit (IC) chip.
[0029] Cores 206 may comprise a shared cache 208 and/or a private
cache (e.g., caches 210-1 and 210-2, respectively). One or more
caches 2 08/210 may locally cache data stored in a system memory,
such as memory 212, for faster access by components of the
processor 202. Memory 212 may be in communication with the
processors 202 via a chipset 216. Cache 208 may be part of system
memory 212 in certain embodiments. Memory 212 may include, but is
not limited to, random access memory (RAM), read only memory (ROM),
and include one or more of solid-state memory, optical or magnetic
storage, and/or any other medium that can be used to store
electronic information. Yet other embodiments may omit system
memory 212.
[0030] System 200 may include one or more I/O devices (e.g., I/O
devices 214-1 through 214-3, each generally referred to as I/O
device 214). I/O data from one or more I/O devices 214 may be
stored at one or more caches 208, 210 and/or system memory 212.
Each of I/O devices 214 may be permanently or temporarily
configured to be in operative communication with a component of
system 100 using any physical or wireless communication
protocol.
[0031] Returning to FIG. 1, four example I/O devices (shown as
elements 116-122) are shown as being in communication with computer
device 114. Those skilled in the art will appreciate that one or
more of devices 116-122 may be stand-alone devices or may be
associated with another device besides computer device 114. For
example, one or more I/O devices may be associated with or interact
with a component of BAN 102 and/or WAN 106. I/O devices 116-122 may
include, but are not limited to athletic data acquisition units,
such as for example, sensors. One or more I/O devices may be
configured to sense, detect, and/or measure an athletic parameter
from a user, such as user 124. Examples include, but are not
limited to: an accelerometer, a gyroscope, a location-determining
device (e.g., GPS), light (including non-visible light) sensor,
temperature sensor (including ambient temperature and/or body
temperature), sleep pattern sensors, heart rate monitor,
image-capturing sensor, moisture sensor, force sensor, compass,
angular rate sensor, and/or combinations thereof among others.
[0032] In further embodiments, I/O devices 116-122 may be used to
provide an output (e.g., audible, visual, or tactile cue) and/or
receive an input, such as a user input from athlete 124. Example
uses for these illustrative I/O devices are provided below,
however, those skilled in the art will appreciate that such
discussions are merely descriptive of some of the many options
within the scope of this disclosure. Further, reference to any data
acquisition unit, I/O device, or sensor is to be interpreted
disclosing an embodiment that may have one or more I/O device, data
acquisition unit, and/or sensor disclosed herein or known in the
art (either individually or in combination).
[0033] Information from one or more devices (across one or more
networks) may be used (or be utilized in the formation of) a
variety of different parameters, metrics or physiological
characteristics including but not limited to: motion parameters, or
motion data, such as speed, acceleration, distance, steps taken,
direction, relative movement of certain body portions or objects to
others, or other motion parameters which may be expressed as
angular rates, rectilinear rates or combinations thereof,
physiological parameters, such as calories, heart rate, sweat
detection, effort, oxygen consumed, oxygen kinetics, and other
metrics which may fall within one or more categories, such as:
pressure, impact forces, information regarding the athlete, such as
height, weight, age, demographic information and combinations
thereof.
[0034] System 100 may be configured to transmit and/or receive
athletic data, including the parameters, metrics, or physiological
characteristics collected within system 100 or otherwise provided
to system 100. As one example, WAN 106 may comprise sever 111.
Server 111 may have one or more components of system 200 of FIG. 2.
In one embodiment, server 111 comprises at least a processor and a
memory, such as processor 206 and memory 212. Server 111 may be
configured to store computer-executable instructions on a
non-transitory computer-readable medium. The instructions may
comprise athletic data, such as raw or processed data collected
within system 100. System 100 may be configured to transmit data,
such as energy expenditure points, to a social networking website
or host such a site. Server 111 may be utilized to permit one or
more users to access and/or compare athletic data. As such, server
111 may be configured to transmit and/or receive notifications
based upon athletic data or other information.
[0035] Returning to LAN 104, computer device 114 is shown in
operative communication with a display device 116, an
image-capturing device 118, sensor 120 and exercise device 122,
which are discussed in turn below with reference to example
embodiments. In one embodiment, display device 116 may provide
audio-visual cues to athlete 124 to perform a specific athletic
movement. The audio-visual cues may be provided in response to
computer-executable instruction executed on computer device 114 or
any other device, including a device of BAN 102 and/or WAN. Display
device 116 may be a touchscreen device or otherwise configured to
receive a user-input.
[0036] In one embodiment, data may be obtained from image-capturing
device 118 and/or other sensors, such as sensor 120, which may be
used to detect (and/or measure) athletic parameters, either alone
or in combination with other devices, or stored information.
Image-capturing device 118 and/or sensor 120 may comprise a
transceiver device. In one embodiment sensor 128 may comprise an
infrared (IR), electromagnetic (EM) or acoustic transceiver. For
example, image-capturing device 118, and/or sensor 120 may transmit
waveforms into the environment, including towards the direction of
athlete 124 and receive a "reflection" or otherwise detect
alterations of those released waveforms. Those skilled in the art
will readily appreciate that signals corresponding to a multitude
of different data spectrums may be utilized in accordance with
various embodiments. In this regard, devices 118 and/or 120 may
detect waveforms emitted from external sources (e.g., not system
100). For example, devices 118 and/or 120 may detect heat being
emitted from user 124 and/or the surrounding environment. Thus,
image-capturing device 126 and/or sensor 128 may comprise one or
more thermal imaging devices. In one embodiment, image-capturing
device 126 and/or sensor 128 may comprise an IR device configured
to perform range phenomenology.
[0037] In one embodiment, exercise device 122 may be any device
configurable to permit or facilitate the athlete 124 performing a
physical movement, such as for example a treadmill, step machine,
etc. There is no requirement that the device be stationary. In this
regard, wireless technologies permit portable devices to be
utilized, thus a bicycle or other mobile exercising device may be
utilized in accordance with certain embodiments. Those skilled in
the art will appreciate that equipment 122 may be or comprise an
interface for receiving an electronic device containing athletic
data performed remotely from computer device 114. For example, a
user may use a sporting device (described below in relation to BAN
102) and upon returning home or the location of equipment 122,
download athletic data into element 122 or any other device of
system 100. Any I/O device disclosed herein may be configured to
receive activity data.
[0038] 2. Body Area Network
[0039] BAN 102 may include two or more devices configured to
receive, transmit, or otherwise facilitate the collection of
athletic data (including passive devices). Exemplary devices may
include one or more data acquisition units, sensors, or devices
known in the art or disclosed herein, including but not limited to
I/O devices 116-122. Two or more components of BAN 102 may
communicate directly, yet in other embodiments, communication may
be conducted via a third device, which may be part of BAN 102, LAN
104, and/or WAN 106. One or more components of LAN 104 or WAN 106
may form part of BAN 102. In certain implementations, whether a
device, such as portable device 112, is part of BAN 102, LAN 104,
and/or WAN 106, may depend on the athlete's proximity to an access
points permit communication with mobile cellular network
architecture 108 and/or WAN architecture 110. User activity and/or
preference may also influence whether one or more components are
utilized as part of BAN 102. Example embodiments are provided
below.
[0040] User 124 may be associated with (e.g., possess, carry, wear,
and/or interact with) any number of devices, such as portable
device 112, shoe-mounted device 126, wrist-worn device 128 and/or a
sensing location, such as sensing location 130, which may comprise
a physical device or a location that is used to collect
information. One or more devices 112, 126, 128, and/or 130 may not
be specially designed for fitness or athletic purposes. Indeed,
aspects of this disclosure relate to utilizing data from a
plurality of devices, some of which are not fitness devices, to
collect, detect, and/or measure athletic data. In certain
embodiments, one or more devices of BAN 102 (or any other network)
may comprise a fitness or sporting device that is specifically
designed for a particular sporting use. As used herein, the term
"sporting device" includes any physical object that may be used or
implicated during a specific sport or fitness activity. Exemplary
sporting devices may include, but are not limited to: golf balls,
basketballs, baseballs, soccer balls, footballs, powerballs, hockey
pucks, weights, bats, clubs, sticks, paddles, mats, and
combinations thereof. In further embodiments, exemplary fitness
devices may include objects within a sporting environment where a
specific sport occurs, including the environment itself, such as a
goal net, hoop, backboard, portions of a field, such as a midline,
outer boundary marker, base, and combinations thereof.
[0041] In this regard, those skilled in the art will appreciate
that one or more sporting devices may also be part of (or form) a
structure and vice-versa, a structure may comprise one or more
sporting devices or be configured to interact with a sporting
device. For example, a first structure may comprise a basketball
hoop and a backboard, which may be removable and replaced with a
goal post. In this regard, one or more sporting devices may
comprise one or more sensors, such one or more of the sensors
discussed above in relation to FIGS. 1-3, that may provide
information utilized, either independently or in conjunction with
other sensors, such as one or more sensors associated with one or
more structures. For example, a backboard may comprise a first
sensors configured to measure a force and a direction of the force
by a basketball upon the backboard and the hoop may comprise a
second sensor to detect a force. Similarly, a golf club may
comprise a first sensor configured to detect grip attributes on the
shaft and a second sensor configured to measure impact with a golf
ball.
[0042] Looking to the illustrative portable device 112, it may be a
multi-purpose electronic device, that for example, includes a
telephone or digital music player, including an IPOD.RTM.,
IPAD.RTM., or iPhone.RTM., brand devices available from Apple, Inc.
of Cupertino, California or Zune.RTM. or Microsoft.RTM. Windows
devices available from Microsoft of Redmond, Wash. As known in the
art, digital media players can serve as an output device, input
device, and/or storage device for a computer. Device 112 may be
configured as an input device for receiving raw or processed data
collected from one or more devices in BAN 102, LAN 104, or WAN 106.
In one or more embodiments, portable device 112 may comprise one or
more components of computer device 114. For example, portable
device 112 may be include a display 116, image-capturing device
118, and/or one or more data acquisition devices, such as any of
the I/O devices 116-122 discussed above, with or without additional
components, so as to comprise a mobile terminal.
[0043] a. Illustrative Apparel/Accessory Sensors
[0044] In certain embodiments, I/O devices may be formed within or
otherwise associated with user's 124 clothing or accessories,
including a watch, armband, wristband, necklace, shirt, shoe, or
the like. These devices may be configured to monitor athletic
movements of a user. It is to be understood that they may detect
athletic movement during user's 124 interactions with computer
device 114 and/or operate independently of computer device 114 (or
any other device disclosed herein). For example, one or more
devices in BAN 102 may be configured to function as an-all day
activity monitor that measures activity regardless of the user's
proximity or interactions with computer device 114. It is to be
further understood that the sensory system 302 shown in FIG. 3 and
the device assembly 400 shown in FIG. 4, each of which are
described in the following paragraphs, are merely illustrative
examples.
[0045] i. Shoe-Mounted Device
[0046] In certain embodiments, device 126 shown in FIG. 1, may
comprise footwear which may include one or more sensors, including
but not limited to those disclosed herein and/or known in the art.
FIG. 3 illustrates one example embodiment of a sensor system 302
providing one or more sensor assemblies 304. Assembly 304 may
comprise one or more sensors, such as for example, an
accelerometer, gyroscope, location-determining components, force
sensors and/or or any other sensor disclosed herein or known in the
art. In the illustrated embodiment, assembly 304 incorporates a
plurality of sensors, which may include force-sensitive resistor
(FSR) sensors 306; however, other sensor(s) may be utilized. Port
308 may be positioned within a sole structure 309 of a shoe, and is
generally configured for communication with one or more electronic
devices. Port 308 may optionally be provided to be in communication
with an electronic module 310, and the sole structure 309 may
optionally include a housing 311 or other structure to receive the
module 310. The sensor system 302 may also include a plurality of
leads 312 connecting the FSR sensors 306 to the port 308, to enable
communication with the module 310 and/or another electronic device
through the port 308. Module 310 may be contained within a well or
cavity in a sole structure of a shoe, and the housing 311 may be
positioned within the well or cavity. In one embodiment, at least
one gyroscope and at least one accelerometer are provided within a
single housing, such as module 310 and/or housing 311. In at least
a further embodiment, one or more sensors are provided that, when
operational, are configured to provide directional information and
angular rate data. The port 308 and the module 310 include
complementary interfaces 314, 316 for connection and
communication.
[0047] In certain embodiments, at least one force-sensitive
resistor 306 shown in FIG. 3 may contain first and second
electrodes or electrical contacts 318, 320 and a force-sensitive
resistive material 322 disposed between the electrodes 318, 320 to
electrically connect the electrodes 318, 320 together. When
pressure is applied to the force-sensitive material 322, the
resistivity and/or conductivity of the force-sensitive material 322
changes, which changes the electrical potential between the
electrodes 318, 320. The change in resistance can be detected by
the sensor system 302 to detect the force applied on the sensor
316. The force-sensitive resistive material 322 may change its
resistance under pressure in a variety of ways. For example, the
force-sensitive material 322 may have an internal resistance that
decreases when the material is compressed. Further embodiments may
utilize "volume-based resistance" may be measured, which may be
implemented through "smart materials." As another example, the
material 322 may change the resistance by changing the degree of
surface-to-surface contact, such as between two pieces of the force
sensitive material 322 or between the force sensitive material 322
and one or both electrodes 318, 320. In some circumstances, this
type of force-sensitive resistive behavior may be described as
"contact-based resistance."
[0048] ii. Wrist-Worn Device
[0049] As shown in FIG. 4, device 400 (which may resemble or
comprise sensory device 128 shown in FIG. 1, may be configured to
be worn by user 124, such as around a wrist, arm, ankle, neck or
the like. Device 400 may include an input mechanism, such as a
depressible input button 402 configured to be used during operation
of the device 400. The input button 402 may be operably connected
to a controller 404 and/or any other electronic components, such as
one or more of the elements discussed in relation to computer
device 114 shown in FIG. 1. Controller 404 may be embedded or
otherwise part of housing 406. Housing 406 may be formed of one or
more materials, including elastomeric components and comprise one
or more displays, such as display 408. The display may be
considered an illuminable portion of the device 400. The display
408 may include a series of individual lighting elements or light
members such as LED lights 410. The lights may be formed in an
array and operably connected to the controller 404. Device 400 may
include an indicator system 412, which may also be considered a
portion or component of the overall display 408. Indicator system
412 can operate and illuminate in conjunction with the display 408
(which may have pixel member 414) or completely separate from the
display 408. The indicator system 412 may also include a plurality
of additional lighting elements or light members, which may also
take the form of LED lights in an exemplary embodiment. In certain
embodiments, indicator system may provide a visual indication of
goals, such as by illuminating a portion of lighting members of
indicator system 412 to represent accomplishment towards one or
more goals. Device 400 may be configured to display data expressed
in terms of activity points or currency earned by the user based on
the activity of the user, either through display 408 and/or
indicator system 412.
[0050] A fastening mechanism 416 can be disengaged wherein the
device 400 can be positioned around a wrist or portion of the user
124 and the fastening mechanism 416 can be subsequently placed in
an engaged position. In one embodiment, fastening mechanism 416 may
comprise an interface, including but not limited to a USB port, for
operative interaction with computer device 114 and/or devices, such
as devices 120 and/or 112. In certain embodiments, fastening member
may comprise one or more magnets. In one embodiment, fastening
member may be devoid of moving parts and rely entirely on magnetic
forces.
[0051] In certain embodiments, device 400 may comprise a sensor
assembly (not shown in FIG. 4). The sensor assembly may comprise a
plurality of different sensors, including those disclosed herein
and/or known in the art. In an example embodiment, the sensor
assembly may comprise or permit operative connection to any sensor
disclosed herein or known in the art. Device 400 and or its sensor
assembly may be configured to receive data obtained from one or
more external sensors.
[0052] iii. Apparel and/or Body Location Sensing
[0053] Element 130 of FIG. 1 shows an example sensory location
which may be associated with a physical apparatus, such as a
sensor, data acquisition unit, or other device. Yet in other
embodiments, it may be a specific location of a body portion or
region that is monitored, such as via an image capturing device
(e.g., image capturing device 118). In certain embodiments, element
130 may comprise a sensor, such that elements 130a and 130b may be
sensors integrated into apparel, such as athletic clothing. Such
sensors may be placed at any desired location of the body of user
124. Sensors 130a/b may communicate (e.g., wirelessly) with one or
more devices (including other sensors) of BAN 102, LAN 104, and/or
WAN 106. In certain embodiments, passive sensing surfaces may
reflect waveforms, such as infrared light, emitted by
image-capturing device 118 and/or sensor 120. In one embodiment,
passive sensors located on user's 124 apparel may comprise
generally spherical structures made of glass or other transparent
or translucent surfaces which may reflect waveforms. Different
classes of apparel may be utilized in which a given class of
apparel has specific sensors configured to be located proximate to
a specific portion of the user's 124 body when properly worn. For
example, golf apparel may include one or more sensors positioned on
the apparel in a first configuration and yet soccer apparel may
include one or more sensors positioned on apparel in a second
configuration.
[0054] FIG. 5 shows illustrative locations for sensory input (see,
e.g., sensory locations 130a-130o). In this regard, sensors may be
physical sensors located on/in a user's clothing, yet in other
embodiments, sensor locations 130a-130o may be based upon
identification of relationships between two moving body parts. For
example, sensor location 130a may be determined by identifying
motions of user 124 with an image-capturing device, such as
image-capturing device 118. Thus, in certain embodiments, a sensor
may not physically be located at a specific location (such as one
or more of sensor locations 130a-1306o), but is configured to sense
properties of that location, such as with image-capturing device
118 or other sensor data gathered from other locations. In this
regard, the overall shape or portion of a user's body may permit
identification of certain body parts. Regardless of whether an
image-capturing device is utilized and/or a physical sensor located
on the user 124, and/or using data from other devices, (such as
sensory system 302), device assembly 400 and/or any other device or
sensor disclosed herein or known in the art is utilized, the
sensors may sense a current location of a body part and/or track
movement of the body part. In one embodiment, sensory data relating
to location 130m may be utilized in a determination of the user's
center of gravity (a.k.a, center of mass). For example,
relationships between location 130a and location(s) 130f/130l with
respect to one or more of location(s) 130m-130o may be utilized to
determine if a user's center of gravity has been elevated along the
vertical axis (such as during a jump) or if a user is attempting to
"fake" a jump by bending and flexing their knees. In one
embodiment, sensor location 130n may be located at about the
sternum of user 124. Likewise, sensor location 130o may be located
approximate to the naval of user 124. In certain embodiments, data
from sensor locations 130m-130o may be utilized (alone or in
combination with other data) to determine the center of gravity for
user 124. In further embodiments, relationships between multiple
several sensor locations, such as sensors 130m-130o, may be
utilized in determining orientation of the user 124 and/or
rotational forces, such as twisting of user's 124 torso. Further,
one or more locations, such as location(s), may be utilized to as a
center of moment location. For example, in one embodiment, one or
more of location(s) 130m-130o may serve as a point for a center of
moment location of user 124. In another embodiment, one or more
locations may serve as a center of moment of specific body parts or
regions.
[0055] FIG. 6 depicts a schematic block diagram of an example
sensor device 600 is configured to dynamically adjust one or more
sampling rates in accordance with certain embodiments. As shown,
sensor device 600 may be embodied with (and/or in operative
communication with) elements configurable to dynamically adjust
sampling rates of the sensor device. In accordance with one
embodiment, by adjusting one or more sampling rates, sensor device
600 can bring about a reduction in power consumption by one or more
integral components. Illustrative sensor device 600 is shown as
having a sensor 602, a filter 604, an analysis processor 606, a
sampling rate processor 608, a memory 610, a power supply 612, a
transceiver 614, and an interface 616. However, one of ordinary
skill in the art will realize that FIG. 6 is merely one
illustrative example of sensor device 600, and that sensor device
600 may be implemented using a plurality of alternative
configurations, without departing from the scope of the processes
and systems described herein. Additionally, sensor device 600 may
include one or more components of computing system 200 of FIG. 2,
wherein sensor device 600 may be considered to be part of a larger
computer device, or may itself be a stand-alone computer device.
Accordingly, in one implementation, sensor device 600 may be
configured to perform, partially or wholly, the processes of
controller 404 from FIG. 4. In such an implementation, sensor
device 600 may be configured to, among other things, bring about a
reduction in power consumption by a wrist-worn device 400 used for
capturing data on an activity being performed by a user, and
thereby, in one embodiment, prolonging a battery life of the device
400.
[0056] In one implementation, power supply 612 may comprise a
battery. Alternatively, power supply 612 may be a single cell
deriving power from stored chemical energy (a group of multiple
such cells commonly referred to as a battery), or may be
implemented using one or more of a combination of other
technologies, including solar cells, capacitors, which may be
configured to store electrical energy harvested from the motion of
device 400 in which sensor device 600 may be positioned, a supply
of electrical energy by "wireless" induction, or a wired supply of
electrical energy from a power mains outlet, such as a universal
serial bus (USB 1.0/1.1/2.0/3.0 and the like) outlet, and the like.
It will be readily understood to one of skill that the systems and
methods described herein may be suited to reducing power
consumption from these, and other power supply 612 embodiments,
without departing from the scope of the description.
[0057] In one implementation, sensor 602 of sensor device 600 may
include one or more accelerometers, gyroscopes,
location-determining devices (GPS), light sensors, temperature
sensors, heart rate monitors, image-capturing sensors, microphones,
moisture sensor, force sensor, compass, angular rate sensor, and/or
combinations thereof among others. As one example embodiment
comprising an accelerometer, sensor 602 may be a three-axis (x-,
y-, and z-axis) accelerometer implemented as a single integrated
circuit, or "chip", wherein acceleration in one or more of the
three axes is detected as a change in capacitance across a silicon
structure of a microelectromechanical system (MEMS) device.
Accordingly, a three-axis accelerometer may be used to resolve an
acceleration in any direction in three-dimensional space. In one
particular embodiment, sensor 602 may include a STMicroelectronics
LIS3DH 3-axis accelerometer package, and outputting a digital
signal corresponding to the magnitude of acceleration in one or
more of the three axes to which the accelerometer is aligned. One
of ordinary skill will understand that sensor 602 may output a
digital, or pulse-wave modulated signal, corresponding to a
magnitude of acceleration. The digital output of sensor 602, such
as one incorporating an accelerometer for example, may be
interpreted as a time-varying frequency signal, wherein a frequency
of the output signal corresponds to a magnitude of acceleration in
one or more of the three axes to which the sensor 602 is sensitive.
In alternative implementations, sensor 602 may output an analog
signal as a time-varying voltage corresponding to the magnitude of
acceleration in one or more of the three axes to which the sensor
602 is sensitive. Furthermore, it will be understood that sensor
602 may be a single-axis, or two-axis accelerometer, without
departing from the scope of the embodiments described herein. In
yet other implementations, sensor 602 may represent one or more
sensors that output an analog or digital signal corresponding to
the physical phenomena to which the sensor 602 is responsive.
[0058] Optionally, sensor device 600 may include a filter 604,
wherein filter 604 may be configured to selectively remove certain
frequencies of an output signal from sensor 602. In one
implementation, filter 604 is an analog filter with filter
characteristics of low-pass, high-pass, or band-pass, or filter 604
is a digital filter, and/or combinations thereof. The output of
sensor 602 is transmitted to filter 604, wherein, in one
implementation, the output of an analog sensor 602 will be in the
form of a continuous, time-varying voltage signal with changing
frequency and amplitude. In one implementation, the amplitude of
the voltage signal corresponds to a magnitude of acceleration, and
the frequency of the output signal corresponds to the number of
changes in acceleration per unit time. Filter 604 may be configured
to remove those signals corresponding to frequencies outside of a
range of interest for activity characterization/recognition and
logging by an activity monitoring device, such as device 400. For
example, filter 604 may be used to selectively remove high
frequency signals over, for example, 100 Hz, which represent motion
of sensor 602 at a frequency beyond human capability. In another
implementation, filter 604 may be used to remove low-frequency
signals from the output of sensor 602 such that signals with a
frequency that is lower than any signal characteristics associated
with a user activity are not processed further by sensor device
600.
[0059] Filter 604 may be referred to as a "pre-filter", wherein
filter 604 may remove one or more frequencies from a signal output
of sensor 602 such that analysis processor 606 does not consume
electrical energy processing data, such as for example, one or more
frequencies not representative of one or more activities being
performed by a user. In this way, pre-filter 604 may reduce overall
power consumption by sensor device 600 or a system of which sensor
device 600 is part of
[0060] In one implementation, the output of filter 604 is
transmitted to both analysis processor 606 and sampling rate
processor 608. When sensor device 600 is powered-on in a first
state and electrical energy is supplied from power supply 612, both
analysis processor 606 and sampling rate processor 608 may receive
a continuous-time output signal from sensor 602, wherein the output
signal may be filtered by filter 604 before being received by
analysis processor 606 and sampling rate processor 608. In another
implementation, when sensor device 600 is powered-on in a second
state, analysis processor 606 and sampling rate processor 608
receive an intermittent signal from sensor 602. Those skilled in
the art will also appreciate that one or more processors (e.g.,
processor 606 and/or 608) may analyze data obtained from a sensor
other than sensor 602.
[0061] Sampling rate processor 608 may, in one implementation, have
a structure similar to processor 202 from FIG. 2, such that
sampling rate processor 608 may be implemented as part of a shared
integrated-circuit, or microprocessor device. In another
implementation, sampling rate processor 608 may be configured as an
application-specific integrated circuit (ASIC), which may be shared
with other processes, or dedicated to sampling rate processor 608
alone. Further, it will be readily apparent to those of skill that
sampling rate processor 608 may be implemented using a variety of
other configurations, such as using discrete analog and/or digital
electronic components, and may be configured to execute the same
processes as described herein, without departing from the spirit of
the implementation depicted in FIG. 6. Similarly, analysis
processor 606 may be configured as an ASIC, or as a general-purpose
processor 202 from FIG. 2, such that both analysis processor 606
and sampling rate processor 608 may be implemented using
physically-separate hardware, or sharing part or all of their
hardware.
[0062] Memory 610, which may be similar to system memory 212 from
FIG. 2, may be used to store computer-executable instructions for
carrying out one or more processes executed by analysis processor
606 and/or sampling rate processor 608. Memory 610 may include, but
is not limited to, random access memory (RAM), read only memory
(ROM), and include one or more of solid-state memory, optical or
magnetic storage, and/or any other medium that can be used to store
electronic information. Memory 610 is depicted as a single and
separate block in FIG. 6, but it will be understood that memory 610
may represent one or more memory types which may be the same, or
differ from one another. Additionally, memory 610 may be omitted
from sensor device 600 such that the executed instructions are
stored on the same integrated circuit as one or more of analysis
processor 606 and sampling rate processor 608.
[0063] Sampling rate processor 608 may be configured to receive
sensor data from sensor 602. In one implementation, upon receipt of
the sensor data, sampling rate processor 608 executes one or more
processes to compare the sensor data to one or more sampling rate
metrics. The sampling rate metrics may include, for example, an
amplitude, magnitude, intensity, or a frequency of the data, and/or
a change in amplitude or frequency, or combinations of any of the
foregoing or other metrics.
[0064] The sensor data received from sensor 602 may represent one
or more of the three axes for which an accelerometer that is part
of sensor 602 is capturing data. Accordingly, sampling rate
processor 608 may process the data from one or more of the three
axes separately and/or may execute a process to average the data
associated with two or more of the axes, which results in an
average amplitude and/or frequency. In one implementation, sampling
rate processor 608 compares the amplitude of the acceleration
signal to one or more threshold sampling rate metrics. By executing
a comparison process, the sampling rate processor 608 may access a
lookup table stored, in one embodiment, in sampling rate processor
608, or alternatively, in memory 610 or any other non-transitory
computer-readable medium. The lookup table may store one or more
sampling rates in combination with one or more respective
acceleration amplitude threshold values. Generally, successively
higher sampling rate values may be stored with successively higher
acceleration amplitude values, wherein it is assumed that, in one
implementation, when sensor device 600 is implemented in, for
example, a wrist worn device 400, more vigorous user activity
results in higher acceleration amplitude data values, and benefits
from higher sampling rates during analysis by an analysis processor
606.
[0065] In one implementation, sampling rate processor 608 executes
a comparison process by iterating through the entries in the lookup
table in ascending order of stored amplitude threshold values. When
the iterating comparison process arrives at an entry in the lookup
table with an amplitude threshold value that is greater than the
received amplitude value from the sensor 602, it selects the
previous, lower, amplitude threshold value, and returns the
sampling rate associated with that selected threshold value.
However, one of ordinary skill in the art will readily understand
that any conventional means of polling/searching through a lookup
table may be employed by the sampling rate processor 608 to select
a sampling rate that is paired with an amplitude threshold value
corresponding to a received amplitude value.
[0066] In an alternative implementation in which sensor 602
comprises an accelerometer, sampling rate processor 608 may compare
a received acceleration data frequency to one or more acceleration
frequency thresholds. In a similar manner to the process described
above, the sampling rate processor may store one or more
acceleration frequency thresholds in combination with one or more
respective sampling rates on a non-transitory computer-readable
medium, such as for example, in a lookup table. The sampling rate
processor 608, may execute an iterative process through the lookup
table until the comparison process arrives at a lookup table entry
with an acceleration frequency threshold value that is greater than
the received frequency value. In response, the comparison process
may select the sampling rate associated with the frequency
threshold value that is closest, and below, the frequency value
arrived at during iteration. Similar to the stored amplitude
threshold value and sampling rate pairs, it may be assumed that
higher frequency data received by the sampling rate processor 608
from sensor 602 corresponds to more vigorous activity of a user,
and may be sampled at higher sampling rates.
[0067] In a further alternative implementation, sampling rate
processor 608 executes one or more processes to iteratively search
a lookup table for a one or more amplitude or frequency
corresponding to one or more stored sampling rates. The sampling
rate processor 608 may return a sampling rate corresponding to an
amplitude or frequency threshold value that is within a range of a
received amplitude or frequency of acceleration from sensor
602.
[0068] In one implementation, the frequency threshold values stored
in a lookup table may correspond to sampling rates in accordance
with the Nyquist sampling theorem (or Nyquist-Shannon sampling
theorem), which states that in order to be able to accurately
reproduce a signal, it should be sampled at a frequency of at least
double the highest frequency present in the signal. For example,
for acceleration data received from sensor 602 that includes a
range of frequencies, ranging from 25 Hz to 100 Hz, the Nyquist
sampling theorem states that in order to accurately reproduce the
received acceleration data, it should be sampled at a sampling rate
of at least 200 Hz. However, in other implementations, the sampling
rates corresponding to stored acceleration frequency thresholds in
a lookup table do not consider the Nyquist sampling theorem.
[0069] In yet another implementation, sampling rate processor 608
may be configured to compare a change in amplitude or a change in
frequency of a data sample from sensor 602. Accordingly, sampling
rate processor 608 may temporarily store one or more amplitude
values from the received acceleration data in memory 610, and
compute the change in amplitude between successively-stored
temporary values, or between a pair or non-successively-stored
temporary values. A lookup table may store amplitude-change
threshold values in combination with respective sampling rates. In
one case, the sampling rate processor 608 returns the sampling rate
corresponding to a stored amplitude-change threshold value that is
closest to, and below, a received acceleration amplitude value from
sensor 602. In another case, sampling rate processor 608 returns
the sampling rate corresponding to a stored amplitude-change
threshold value that is within a range of a received acceleration
amplitude value from sensor 602. Similarly, the sampling rate
processor 608 may compute a change in frequency for one or more
temporarily-stored accelerometer data points, and compare one or
more changes in frequency to stored frequency-change threshold
values in a lookup table. An iterative search by sampling rate
processor 608 through the lookup table may return a sampling rate
corresponding to a frequency-change threshold that is, closest to
and below, or equal to, or is within a certain range of, a stored
frequency-change threshold value.
[0070] Furthermore, sampling rate processor 608 may be configured
to selectively compare one or more of an amplitude, frequency,
amplitude change, or frequency change, among others, from an
acceleration data sample using a single lookup table with
thresholds, and corresponding sampling rates, stored for one or
more of amplitude, frequency, amplitude change, and frequency
change.
[0071] In view of the foregoing, it will be readily apparent to one
of ordinary skill in the art that the systems and processes
described herein, and in one implementation, executed by sampling
rate processor 608, can alternatively be implemented using sensor
data from sensors other than sensor 602. In one alternative
implementation, threshold values of light (electromagnetic
radiation) intensity or light frequency from a light sensor 602 may
be compared to a received light intensity value or light frequency
value, or changes in intensity or frequency. Using a similar
process to that described in relation to sensor 602, sampling rate
processor 608 may query a lookup table for sampling rates
corresponding to received light values. Furthermore, and as
previously described, sensor device 600 may be implemented with one
or more of a variety of sensor types in addition to an
accelerometer or light sensor. Accordingly, the processes executed
by sampling rate processor 608 may evaluate output values from the
respective different sensor types in a similar manner to that
described in relation to both the accelerometer and the light
sensor.
[0072] It is assumed that in certain embodiments, analysis
processor 606 may consume a significant portion of the total energy
used by sensor device 600, when sampling and analyzing sensor data
at a high, or upper sampling rate. For example, analysis processor
606 may sample sensor data from sensor 602 at an upper sampling
rate of 50 Hz, and consume 95% of the total electrical energy of
sensor device 600. It is further assumed that using a sampling rate
that is below a high, or upper sampling rate associated with
analysis processor 606 can lead to significant reductions in power
consumption. For example, if the sampling rate of the analysis
processor 606 is reduced to 24 Hz, the power consumption of sensor
device 600 is reduced by 50%. Where power supply 612 is implemented
as a battery, this reduction in power consumption can lead to
significant increases in battery life between recharges. For
example, where the sampling rate of analysis processor 606 is
reduced from 50 Hz to 24 Hz, the battery life is doubled.
Advantageously, and for a sensor device 600 positioned in a
wrist-worn device 400, this may allow a user to wear device 400 for
longer periods of time without needing to remove device 400 for
recharging.
[0073] In one implementation, sampling rate processor 608 receives
data from sensor 602 and selects a sampling rate before analysis
processor 606 processes said same accelerometer data. The sampling
rates selected by sampling rate processor 608 are selected such
that they reduce the power consumption of analysis processor 606
while maintaining a sampling rate high enough that the processes
executed by analysis processor 606 receive information
representative of the output from the sensor 602. As such, the
sampling rate is maintained at a rate that may be used to
accurately interpret activity metrics from the received sensor
data. In this regard, device 600 may have a default sampling rate
that is less than the highest achievable sampling rate. In one
embodiment, the sampling rate processor 608 executes one or more
processes on accelerometer data, selects a sampling rate, and
transmits this sampling rate to analysis processor 606. In one
implementation, the transmitted sampling rate may range from 0 Hz
to 50 Hz, wherein 50 Hz corresponds to an exemplary high, or upper
sampling rate for analysis processor 606. In another embodiment,
sampling rate processor 606 may transmit a sampling rate ranging
from 0 Hz to 100 Hz or 0 Hz to 500 Hz and any other range.
[0074] In another implementation, if sampling rate processor 608
determines that a sensed value, such as for example, an
acceleration amplitude or frequency value, is not above a stored
amplitude or frequency threshold value, it may execute a process to
instruct analysis processor 606 not to analyze the acceleration
data. This may be the case when, for example, the sensor device 600
is moved briefly, but the movement does not correspond to an
activity (or specific type of activity) being performed by a user.
This instruction not to analyze the received sensor data may be
explicit, with a transmission of a sampling rate of 0 Hz from
sampling rate processor 608 to analysis processor 606, or an
equivalent instruction not to sample data from sensor 602.
Alternatively, the instruction may be implicit, such that sampling
rate processor 608 does not transmit instructions to analysis
processor 606 if no analysis is to be performed. In this way,
analysis processor 606 may remain in a "sleep" state until data of
interest (e.g., a threshold level of movement along one or more
axis for a first time period) is received. Analysis processor 606
may subsequently be "woken" from this sleep state when sampling
rate processor 608 transmits a signal to the analysis processor
606. This wake signal may be a sampling rate frequency, or may be
transmitted as a separate message, to a same, or a different input
to that receiving a sampling rate. While in the sleep state,
analysis processor 606 may consume no energy, or may consume an
amount of energy to keep a process active to listen for a wake
signal from the sampling rate processor 608.
[0075] Analysis processor 606 may be configured to execute
processes to recognize one or more activities being carried out by
a user, and to classify the one or more activities into one or more
activity categories. In one implementation, activity recognition
may include quantifying steps taken by the user based upon motion
data, such as by detecting arm swings peaks and bounce peaks in the
motion data. The quantification may be done based entirely upon
data collected from a single device worn on the user's arm, such as
for example, proximate to the wrist. In one embodiment, motion data
is obtained from an accelerometer. Accelerometer magnitude vectors
may be obtained for a time frame and values, such as an average
value from magnitude vectors for the time frame may be calculated.
The average value (or any other value) may be utilized to determine
whether magnitude vectors for the time frame meet an acceleration
threshold to qualify for use in calculating step counts for the
respective time frame. Acceleration data meeting a threshold may be
placed in an analysis buffer. A search range of acceleration
frequencies related to an expected activity may be established.
Frequencies of the acceleration data within the search range may be
analyzed in certain implementations to identify one or more peaks,
such as a bounce peak and an arm swing peak. In one embodiment, a
first frequency peak may be identified as an arm swing peak if it
is within an estimated arm swing range and further meets an arm
swing peak threshold. Similarly, a second frequency peak may be
determined to be a bounce peak if it is within an estimated bounce
range and further meets a bounce peak threshold.
[0076] Furthermore, systems and methods may determine whether to
utilize the arm swing data, bounce data, and/or other data or
portions of data to quantify steps or other motions. The number of
peaks, such as arm swing peaks and/or bounce peaks may be used to
determine which data to utilize. In one embodiment, systems and
methods may use the number of peaks (and types of peaks) to choose
a step frequency and step magnitude for quantifying steps. In still
further embodiments, at least a portion of the motion data may be
classified into an activity category based upon the quantification
of steps.
[0077] In one embodiment, the sensor signals (such as accelerometer
frequencies) and the calculations based upon sensor signals (e.g.,
a quantity of steps) may be utilized in the classification of an
activity category, such as either walking or running, for example.
In certain embodiments, if data cannot be categorized as being
within a first category (e.g., walking) or group of categories
(e.g., walking and running), a first method may analyze collected
data. For example, in one embodiment, if detected parameters cannot
be classified, then a Euclidean norm equation may be utilized for
further analysis. In one embodiment, an average magnitude vector
norm (square root of the sum of the squares) of obtained values may
be utilized. In yet another embodiment, a different method may
analyze at least a portion of the data following classification
within a first category or groups of categories. In one embodiment,
a step algorithm, may be utilized. Classified and unclassified data
may be utilized to calculate an energy expenditure value in certain
embodiments.
[0078] Exemplary systems and methods that may be implemented to
recognize one or more activities are described in U.S. patent
application Ser. No. 13/744,103, filed Jan. 17, 2013, the entire
content of which is hereby incorporated by reference herein in its
entirety for any and all non-limited purposes. In certain
embodiments, activity processor 606 may be utilized in executing
one or more of the processes described in the herein including
those described in the '103 application.
[0079] The processes used to classify the activity of a user may
compare the data received from sensor 602 to a stored data sample
that is characteristic of a particular activity, wherein one or
more characteristic data samples may be stored in memory 610.
[0080] In one implementation, the activity recognition process and
data logging may be executed by analysis processor 606
independently of an initial selection of a sampling rate by
sampling rate processor 608 using data received from sensor 602 by
the sampling rate processor 608. In this implementation, the
activity recognition process may be executed using an initial
sampling rate that lies in the middle of a sampling rate range
available to the analysis processor 606. In another implementation,
the sampling rate processor 608 executes processes to select a
sampling rate, and communicates a selected sampling rate with which
the analysis processor 606 initializes activity recognition.
[0081] The activity recognition processes carried out by analysis
processor 606 may result in one or more of a number of outcomes,
including; the activity being performed by a user is recognized
within a certain confidence interval, or the activity is not
recognized. In one implementation, if an activity cannot be
recognized by analysis processor 606 after analysis processor 606
processes data sampled at a first sampling rate, the analysis
processor 606 sends instructions to sampling rate processor 608 to
incrementally increase the sampling rate. In response, sampling
rate processor 608 increments the sampling rate, wherein it is
assumed that increasing the sampling rate may increase the
likelihood of a positive activity recognition outcome. This
activity recognition process may be iterative, such that if an
activity is not recognized following an increment in sampling rate,
the analysis processor 606 instructs sampling rate processor 608 to
increment the sampling rate again, and so on, until an upper
sampling rate for the analysis processor 606 is reached. In this
way, the sampling rate processor 608 attempts to find a low
sampling rate to reduce power consumption. However, sampling rate
processor 608 attempts to find the low sampling rate, otherwise
referred to as a sampling resolution, still high enough to capture
data representative of an activity being performed by a user, and
such that an activity recognition process will be successful using
data captured as the low sampling rate.
[0082] In another implementation, if the analysis processor 606 is
successful at recognizing an activity being carried out by a user,
the analysis processor 606 may instruct the sampling rate processor
608 to decrement the sampling rate. This process of decreasing the
sampling rate may continue in an iterative manner until the
analysis processor 606 can no longer recognize the incoming
accelerometer data.
[0083] In another embodiment, the sampling rate processor 608, upon
successful or unsuccessful completion of an activity recognition
process by analysis processor 606, may execute instructions
requesting data from one or more additional sensors 602, wherein
the additional sensors may be used to better characterize an
activity being performed by a user. Those additional sensors may
include one or more of accelerometers, gyroscopes,
location-determining devices (GPS), light sensors, temperature
sensors, heart rate monitors, image-capturing sensors, microphones,
moisture sensor, force sensor, compass, angular rate sensor, or
combinations thereof. In yet another embodiment, sampling rate
processor 608, upon successful or unsuccessful completion of an
activity recognition process, may execute instructions requesting
receipt of sensor data from one or more sensors instead of a
currently-active sensor 602. In certain embodiments, a first sensor
may be adjusted to a first sampling rate and a second sensor may be
adjusted to a second sampling rate based upon the sampling rate of
the first sensor and/or determination of activity.
[0084] Upon successful recognition of an activity being carried out
by a user, analysis processor 606 may log samples of the activity
data, or execute other processes on the sampled data to extract
performance metrics from the data. These logged samples, or
extracted performance metrics, may be stored in memory 610.
[0085] In yet another implementation, sampling rate processor 608
may execute one or more processes to instruct analysis processor
606 to sample data from sensor 602 at a sampling rate corresponding
to a low stored-energy level in power supply 612. This low-battery
sampling rate is intended to reduce power consumption by the
analysis processor 606 while maintaining a sampling rate that is
high enough to capture data representative of an activity being
performed by a user.
[0086] Sensor device 600 may optionally have a transceiver 614, for
communicating stored performance metrics, samples of activity data,
and the like, to a computer device, as described in relation to
sensor 120 from FIG. 1. Additionally, sensor device 600 may also be
configured to have an interface 616, facilitating a physical
connection to another device, as described in relation to I/O
device 214 from FIG. 2.
[0087] FIG. 7 is a schematic block diagram depicting a more
detailed implementation of sampling rate processor 608 from FIG. 6.
In particular, FIG. 7 includes a sampling rate processor 608, a
sensor data input 702, an analog-to-digital convertor 704, a
register 706, a logic circuit 708, an output 710, an input 712, and
analysis processor 606.
[0088] In one embodiment, sampling rate processor 608 is
implemented with an analog-to-digital convertor 704.
Analog-to-digital convertor 704 may be employed to convert an
analog signal, such as analog sensor data 702 received from sensor
602, into a digital output signal. The digital output from
analog-to-digital convertor 704 is transmitted to memory register
706 such that sampling rate processor 608 stores a number of
samples of sensor data 702 in digital form. Register 706 may be
implemented using a variety of embodiments well known in the art.
Logic circuit 708 may be a special-purpose digital circuit
comprising a plurality of digital logic gates configured to carry
out the sample rate selection processes described in relation to
FIG. 6. Alternatively, logic circuit 708 may be a general-purpose
array of transistors similar to that of processor 202 from FIG.
2.
[0089] Logic circuit 708 may output, among others, a sampling rate
at output 710, wherein output 710 is transmitted to input 712 of
analysis processor 606. Input 712 may be physical or logical input
to analysis processor 606. In one implementation, input 712 is a
specific pin of an ASIC, configured to receive a signal
corresponding to an instruction for analysis processor 606 to
sample sensor data 702 at a specific sampling rate.
[0090] FIG. 8 is a flowchart diagram of an analysis activation
process 800 in accordance with one embodiment. Process 800 or a
portion thereof may be executed by sensor device 600 from FIG. 6.
Process 800 may be initiated at block 802, wherein sensor device
600 is powered on and power supply 612 is supplying electrical
energy to one or more of components 602-616. While sensor device
600 is powered on, analysis processor 606 may optionally be in a
sleep state, wherein while in the sleep state, analysis processor
606 is not sampling data from sensor 602. Consequently, the sleep
state facilitates low power consumption by the analysis processor
606 from power supply 612. Analysis processor 606 may execute
processes to enter into the sleep state upon initialization of
sensor device 600, or optionally, analysis processor 606 may be
instructed to enter into the sleep state at any time, including by
a process executed from sampling rate processor 608. In one
implementation, sampling rate processor 608 will execute processes
to instruct analysis processor 606 to sleep upon expiration of a
timeout period between receipt of sensor data from sensor 602 that
is above a threshold value.
[0091] It may be determined whether a sensor signal is received
(e.g., see decision 804). Decision 804 may represent a timeout
period during which sampling rate processor 608 is awaiting arrival
of new sensor data from sensor 602. For example, sampling rate
processor may check (e.g. periodically or based upon an input) for
new data, and if no data has been received, the timeout period
follows path 806, looping back to block 804. If sampling rate
processor 608 is in receipt of new data, path 808 may be followed.
The received data may optionally be passed through a filter, such
as filter 604, at block 810. The filtered and/or unfiltered sensor
data may be further processed by sampling rate processor 608. For
example, decision 812 may be implemented to perform one or more
comparison processes carried out by sampling rate processor 608,
described in further detail in relation to FIG. 6. In one
embodiment, the one or more comparison processes may compare the
sensor data to one or more threshold values. In one embodiment, if
a magnitude of the sensor data signal is not above a threshold
value, process 800 may return to block 804 along path 814.
Alternatively, if a parameter of the sensor data (e.g. the
magnitude of the sensor data) is above a threshold value, sampling
rate processor 608 may execute a process to wake analysis
processor, and to sample the sensor data at a specific sampling
rate (see, e.g., block 818 via path 816). In this way, process 800
may be seen in certain embodiments as a method regarding the
activation, or waking, of analysis processor 606 from a sleep mode,
such that analysis processor 606 consumes a low amount of energy up
until sensor data above a threshold value is received.
[0092] FIG. 9 is flowchart diagram of a process 900 for adjustment
of a sampling rate, such by, for example sampling rate processor
608, in response to receipt of a sensor data signal that has a
magnitude above one or more threshold values. Process 900 may be
initiated at block 902 as a processor (e.g., sampling rate
processor 608) receives sensor data. Simultaneously, another
processor (e.g., analysis processor 606) may be sampling the sensor
data at a default initialization sampling rate, or predetermined
sampling rate. Process 900 proceeds to block 904 wherein sampling
rate processor 608 executes one or more comparison processes on the
sensor data. These comparison processes may compare an amplitude, a
frequency, a change in amplitude, or a change in frequency, among
others, of the sensor data signal to one or more threshold values,
as described in relation to FIG. 6. Upon calculation, by the
sampling rate processor 608, of a threshold value corresponding to,
or within a predetermined range of, a value of the sensor data
signal, process 900 may proceed to block 906.
[0093] Decision 906 may be implemented to determine whether to
change or otherwise alter the sampling rate. In this regard,
decision 906 may be implemented as a result of sampling rate
processor 608 calculating that a magnitude of the sensor data
signal corresponds to a threshold value from a plurality of
threshold values. The corresponding threshold value has an
associated sampling rate value that may be above, below, or equal
to a current sampling rate in-use by analysis processor 606. In
another implementation, block 904 may compare a magnitude of a
sensor data value to a single threshold value. This comparison may
result in an instruction from sampling rate processor 608 for
analysis processor 606 to adjust its sampling rate to a specific
value, or alternatively, when a comparison to a single threshold is
employed, to increment or decrement the sampling rate by a
predetermined amount. If the current sampling rate in use by
analysis processor 906 is less than that specific sampling rate
selected, or if the magnitude of the sensor data value is greater
than a single threshold value used in the comparison, sampling rate
processor may instruct analysis processor 606 to increase its
sampling rate, and process 900 may proceed along path 908 to
comparison block 904. Conversely, if the current sampling rate used
by analysis processor 606 is greater than the calculated sampling
rate, or if the magnitude of the sensor signal value is below a
single threshold value used for comparison, processor 900 may
proceed along path 910, and sampling rate processor may 608
instruct analysis processor 606 to decrease the sampling rate,
wherein the decrease in sampling rate may be to a specific sampling
rate, or by a predetermined, decrement amount. Paths 908 and 910
represent an iterative loop through blocks 904 and 906 until
process 900 arrives at a target sampling rate, wherein the target
sampling rate is transmitted to analysis processor 606 for sampling
of the sensor data at block 914.
[0094] FIG. 10 is a flowchart of a process 1000 that may be
utilized in the adjustment of sampling conditions in response to
activity recognition. As shown in FIG. 10, block 1002 may be
implemented to receive sensor data. For example, analysis processor
606 may be sampling a signal from a sensor (e.g. sensor 602). In
further embodiments, sensor data received at 1002 may be filtered
and/or processed sensor data. In one embodiment, only data passing
a threshold may be received or otherwise considered at block 1002.
As one example, process 1000 may have one or more aspects that are
similar or identical to decision 812 when determining what data is
received or utilized. Sensor data may be analyzed using one or more
activity recognition processes such as described above in relation
to analysis processor 606 (see, e.g. block 1004). Decision 1006 may
be implemented to determine whether one or more activity
recognition processes were successful at classifying the sensor
data into an activity classification. In response to the activity
recognition processes being unsuccessful, path 1012 may be followed
to block 1014. Block 1014 may increase the sampling rate at which
sensor data from at least one is sampled, and/or may instruct one
or more additional or alternative sensors to be used to capture
information about the activity being performed. In one embodiment,
block 1014 may be performed, at least partially, by analysis
processor 606. Those skilled in the art will appreciate that block
1014 is merely an example. Other embodiments may retain the current
sampling rate and/or selected sensors. In certain embodiments, the
number of sensors utilized and/or the sampling rate may be
decreased. For example, if a specific activity (or type of
activity) is not detected, the sampling rate may decrease to
preserve battery life. In certain embodiments, parameters of the
data may indicate whether the sampling rate or number of sensors is
altered (either increased or decreased). For example, large amounts
of motion from a single axis may be treated differently than lower
quantities of motion from multiple axes.
[0095] Looking back to decision 1006, if the one or more activity
recognition processes are successful in classifying the activity of
a user into an activity classification, process 1000 proceeds from
decision point 1006 to block 1010, wherein the sampling rate or
quantity of sensors utilized may be adjusted in a manner than would
be performed if block 1014 was implemented. For example, in one
embodiment, sampling rate processor 608 may instruct analysis
processor 606 to decrease its sampling rate, or change the number
or type of sensors from which information about the activity of the
user is being captured. In this way, a decrease in sampling rate,
use of alternative sensors more capable of capturing data related
to the determined activity, or use of a lower number of sensors,
sensor device 600 is configured to consume less power.
[0096] In further embodiments, further data is collected at the
adjusted sampling rate from the selected sensor(s), which may be
compared to one or more threshold values. In one embodiment, one or
more processes similar or identical to block 804 may be
implemented. Thus, the data may be compared to a threshold value
periodically or after a first time interval (e.g., every 1 second
or 5 seconds) to determine whether to adjust the sampling rate
and/or sensors utilized without regard to whether the activity has
changed in the meantime. For example, activity determinations may
only be conducted after a duration that is longer than the first
time frame (e.g., every 10 seconds). Thus, two or more variables
(e.g., threshold levels of sensor data and activity determinations)
may be utilized independently to adjust the sampling rate or
sensors utilized. Those skilled in the art will appreciate that
activity determinations may be performed at a time interval that is
less than threshold level determinations. In this regard, a first
sensor may be utilized at a first sampling rate if a first
threshold level is obtained when a first activity is detected and
not at all if a second activity is detected regardless of whether a
threshold level of sensor data is obtained. Similarly, a second
sensor may be utilized regardless of what activity is sensed and
the sampling rate may be influenced by the threshold level of
sampling data.
[0097] FIG. 11 is a flowchart diagram of process 1100 which may be
executed by, in one embodiment, one or more components of sensor
device 600 from FIG. 6, among others. In one implementation,
analysis processor 606 from FIG. 6 may receive data at a first
sampling rate, as indicated by block 1110. The data received at the
first sampling rate may be representative of, among others, an
activity being carried out by a user of sensor device 600, wherein
the activity being carried out may be a sporting activity. The
first sampling rate may be determined by sampling rate processor
608 based on, among others, a default sampling rate communicated
from sampling rate processor 608 to analysis processor 606 upon
initialization of sensor device 600. In another implementation, the
first sampling rate may be a last-used sampling rate, such as for
example, by analysis processor 606, as communicated to analysis
processor 606 by sampling rate processor 608, wherein the last-used
sampling rate may be, in one implementation, the sampling rate at
which a processor, such as analysis processor 606, sampled data
prior to sensor device 600 being powered-off, or instructed to
enter into a sleep mode.
[0098] Block 1120 may be implemented to select or classify the
received data into an activity category, wherein an activity
category is representative of one or more activities being carried
out by a user being monitored by sensor device 600, among others.
The user may be wearing sensor device 600, yet in other
embodiments, a camera and/or other sensors may be utilized to
monitor the user's activity without being in physical communication
with the user. The selection of a category or classification of the
received data into an activity category may be based upon, among
others, a selected activity category by a user, a recognized
activity category, wherein one or more activity recognition
processes may be executed by analysis processor 606. The selected
activity category may also be a default activity category, wherein
the default activity category may be selected by analysis processor
606 upon initialization of sensor device 600, or a last-known
activity category used by analysis processor 606.
[0099] In another implementation, as indicated as block 1130, the
data received, such as by analysis processor 606, may be compared
to one or more threshold values. For example, the received data may
have one or more numerical values such that one or more processes
may be executed by analysis processor 606, among others, to
determine if the one or more numerical values are within a range of
a first threshold value, closest to, but above a first threshold
value, or equal to a first threshold value, among others. In one
embodiment, and in response to determining, such as by analysis
processor 606, that one or more of the received numerical values
corresponds to one or more first threshold values, the received
data may be classified into one or more activity categories, such
as by analysis processor 606. Selection or classification of the
received data into one or more activity categories may be based on
the corresponding first threshold values, wherein the one or more
first threshold values further corresponds to one or more activity
categories.
[0100] In another embodiment, data may be received, such as by
analysis processor 606, at a second sampling rate. The second
sampling rate may be selected by sampling rate processor 608, such
that sensor device 600 may, among others, consume less power, or
receive data at a sampling rate that is representative of the
activity being carried out by a user. Block 1140 represents one or
more processes for receiving data at a second sampling rate. In one
implementation, the second sampling rate may be based on at least
one or more activity categories into which the data received from
the user was classified, such as by analysis processor 606, and as
described in relation to block 1120. In another implementation, the
second sampling rate is based on at least one or more threshold
values corresponding to the received data, wherein the first
threshold values are described in relation to block 1130. The
second sampling rate may, in one implementation, be higher than the
first sampling rate, such that data is sampled more frequently. In
yet another implementation, however, the second sampling rate may
be lower than the first sampling rate, such that data is sampled
less frequently, and there is a corresponding decrease in power
consumption by, among others, analysis processor 606.
[0101] Block 1150 represents one or more processes corresponding to
a selection, such as by sampling rate processor 608, of one or more
second threshold values. The one or more second threshold values
are selected, by sampling rate processor 608, based on the one or
more first threshold values, or a classification of activity data
into an activity classification, or combination thereof. In one
implementation, the one or more second threshold values are
selected in response to the received data having a numerical value
above, within a predefined range of, or equal to a first threshold
value, and the received data being classified into an activity
classification. In this way, when the received data corresponds to
one or more first threshold values, in combination with the
received data are being classified into one or more activity
classifications, one or more new, or second, sampling rates may be
selected (such as by sampling rate processor), and re-evaluates the
received data. In one exemplary embodiment, the processes
corresponding to block 1150 may be executed if, for example, a
value of data received is above a threshold corresponding to
vigorous activity, and the received data has been classified into,
for example, an activity classification corresponding to playing
basketball. In response, sampling rate processor 608 may select a
second threshold value corresponding to light activity, and
sampling rate processor 608 may not adjust the sampling rate of
analysis processor 606 until data is received with a value
corresponding to this light activity threshold.
[0102] Block 1160 corresponds to one or more processes, executed by
sampling rate processor 608, wherein sampling rate processor 608
may select one or more sensors to receive activity data from. In
one implementation, the one or more sensors selected may be in
addition to one or more currently-used sensors from which data is
received by analysis processor 606 at block 1110. In another
implementation, the one or more selected sensors may replace the
one or more currently-used sensors from which data is received at
block 1110. The one or more sensors selected at block 1160 may be
selected based on, among others, the second sampling rate, or the
activity classification into which the received data has been
classified, wherein the one or more sensors may be selected for
being relatively more efficient and/or effective at receiving data
corresponding to the activity classification or the second sampling
rate.
For the avoidance of doubt, the present application extends to the
subject-matter described in the following numbered paragraphs
(referred to as "Para" or "Paras"): [0103] 1. A unitary apparatus
configured to be worn by a user, comprising: [0104] a power supply;
[0105] a sensor arranged to capture acceleration data from an
appendage of the user; [0106] a sampling rate processor arranged to
receive the captured acceleration data and determine a first
sampling rate; and [0107] an analysis processor arranged to sample
the data captured by the sensor at the first sampling rate and
analyze the sampled data so as to classify the acceleration data
into an activity category being performed by the user; [0108]
wherein the sampling rate processor attempts to choose the first
sampling rate with a value below an upper sampling rate in order to
reduce power consumption by the analysis processor from the power
supply during sampling. [0109] 2. The unitary apparatus of Para 1,
wherein the sampling rate processor is further configured to:
[0110] compare a value of the acceleration data to a threshold
value; and [0111] determine the first sampling rate as
corresponding to the threshold value. [0112] 3. The unitary
apparatus of Para 2, wherein the sampling rate processor determines
the first sampling rate corresponding to the threshold value when
the value of the acceleration data is equal to the threshold value.
[0113] 4. The unitary apparatus of Para 2, wherein the sampling
rate processor determines the first sampling rate corresponding to
the threshold value when the value of the acceleration data is
numerically closer to, and greater than, a second threshold value.
[0114] 5. The unitary apparatus of Para 2, wherein the sampling
rate processor determines the first sampling rate corresponding to
the threshold value when the value of the acceleration data is
within a range of the threshold value. [0115] 6. The unitary
apparatus of any of Paras 2 to 5, wherein the value of the
acceleration data is an amplitude. [0116] 7. The unitary apparatus
of any of Paras 2 to 5, wherein the value of the acceleration data
is a frequency. [0117] 8. The unitary apparatus of any preceding
Para, wherein the sampling rate processor determines the first
sampling rate as a low-battery sampling rate corresponding to a low
level of stored electrical energy in the power supply. [0118] 9.
The unitary apparatus of any preceding Para, wherein the analysis
processor is further configured to store sampled acceleration data
corresponding to the classified activity category in a
non-transitory computer-readable medium. [0119] 10. The unitary
apparatus of any preceding Para, wherein the sampling rate
processor is further configured to: [0120] determine a second
sampling rate corresponding to the activity category into which the
acceleration data is classified, and in response to the determined
second sampling rate, storing, by the analysis processor,
acceleration data sampled at the second sampling rate. [0121] 11.
The unitary apparatus of Para 10, wherein the second sampling rate
corresponds to a low power consumption rate by the analysis
processor, while maintaining a sampling resolution to capture data
for the classified activity category. [0122] 12. The unitary
apparatus of any preceding Para, further comprising: [0123] a
filter, for selectively filtering out a signal from the captured
acceleration data. [0124] 13. The unitary apparatus of any
preceding Para, further comprising a memory register circuit which
is arranged to store the captured acceleration data received by the
sampling rate processor. [0125] 14. The unitary apparatus of any
preceding Para, wherein the sampling rate processor is further
configured to: [0126] select, in response to the classification of
the acceleration data into an activity category, a second sensor
from which to capture data about the activity of the user. [0127]
15. The unitary apparatus of any preceding Para, wherein the
sampling rate processor is further configured to: [0128] select, in
response to receipt of the captured acceleration data, a second
sensor from which to capture data about the activity of the user.
[0129] 16. The unitary apparatus of any preceding Para, further
comprising: [0130] a transceiver, for communicating the sampled
data to a portable computer system. [0131] 17. The unitary
apparatus of any preceding Para, wherein the first sampling rate
ranges from 0 Hz to 50 Hz. [0132] 18. A computer-implemented method
for reducing power consumption by a sensor apparatus, comprising:
[0133] capturing, by a sensor located on a device configured to be
worn on an appendage of a user, acceleration data for the appendage
of the user; [0134] receiving, by a sampling rate processor of the
device, the captured acceleration data; [0135] determining, by the
sampling rate processor, a first sampling rate for the sensor, by
selecting the first sampling rate that is below an upper sampling
rate in order to reduce power consumption by an analysis processor
during sampling; [0136] sampling, by the analysis processor, data
captured by the sensor at the first sampling rate; and [0137]
analyzing, by the analysis processor, the sampled data in an
attempt to classify the acceleration data into an activity category
being performed by the user. [0138] 19. The method according to
Para 18, further comprising: [0139] comparing, by the sampling rate
processor, a value of the acceleration data to a threshold value;
and [0140] determining, by the sampling rate processor, the first
sampling rate as corresponding to the threshold value. [0141] 20.
The method according to Para 19, further comprising: [0142]
determining, by the sampling rate processor, the first sampling
rate corresponding to the threshold value when the value of the
acceleration data is equal to the threshold value. [0143] 21. The
method according to Para 19, further comprising: [0144]
determining, by the sampling rate processor, the first sampling
rate corresponding to the threshold value when the value of the
acceleration data is numerically closer to, and greater than, a
second threshold value. [0145] 22. The method according to 19,
further comprising: [0146] determining, by the sampling rate
processor, the first sampling rate corresponding to the threshold
value when the value of the acceleration data is within a range of
the threshold value. [0147] 23. The method according to Para 19,
wherein the value of the acceleration data is an amplitude. [0148]
24. The method according to Para 19, wherein the value of the
acceleration data is a frequency. [0149] 25. The method according
to any of Paras 18 to 24, further comprising: [0150] determining,
by the sampling rate processor, the first sampling rate as a
low-battery sampling rate corresponding to a low level of stored
electrical energy in the power supply. [0151] 26. The method
according to any of Paras 18 to 25, further comprising: [0152]
storing, by the analysis processor, sampled acceleration data
corresponding to the classified activity category in a
non-transitory computer-readable medium. [0153] 27. The method
according to any of Paras 18 to 26, further comprising: [0154]
determining, by the sampling rate processor, a second sampling rate
corresponding to the activity category into which the acceleration
data is classified, and in response to the determined second
sampling rate, storing, by the analysis processor, acceleration
data sampled at the second sampling rate. [0155] 28. The method
according to Para 27, wherein the second sampling rate corresponds
to a low power consumption rate by the analysis processor, while
maintaining a sampling resolution to capture data for the
classified activity category. [0156] 29. The method according to
any of Paras 18 to 28, further comprising: [0157] selectively
filtering out a signal from the captured acceleration data, by a
filter. [0158] 30. The method according to any of Paras 18 to 29,
further comprising: [0159] receiving, by the sampling rate
processor, the captured acceleration data into a memory register
circuit. [0160] 31. The method according to any of Paras 18 to 30,
further comprising: [0161] selecting, by the sampling rate
processor, and in response to the classification of the
acceleration data into an activity category, a second sensor from
which to capture data about the activity of the user. [0162] 32.
The method according to any of Paras 18 to 30, further comprising:
[0163] selecting, by the sampling rate processor, and in response
to receipt of the captured acceleration data, a second sensor from
which to capture data about the activity of the user. [0164] 33.
The method according to any of Paras 18 to 32, wherein the first
sampling rate ranges from 0 Hz to 50 Hz. The present application
also extends to the subject-matter described in the following
numbered paragraphs: [0165] 1. A non-transitory computer-readable
medium comprising computer-executable instructions that when
executed by a processor is configured to perform at least: [0166]
receiving acceleration data representing movement of an appendage
of a user at a sampling rate processor located on a device
configured to be worn on an appendage of a human, wherein the
acceleration data was obtained by an accelerometer located on the
device that is operating at a first sampling rate; [0167]
classifying the acceleration data into one of a plurality of
activity categories representing an activity being performed by the
user; and [0168] based upon at least the classified activity
category, selecting a second sampling rate for operating the
accelerometer. [0169] A2. The non-transitory computer-readable
medium of Para 1, wherein the medium further comprises
computer-executable instructions that when executed further perform
at least: [0170] comparing a first value of acceleration data
obtained from the accelerometer during operation at the first
sampling rate against a plurality of threshold values; [0171]
determining that the first value of acceleration data corresponds
to a first threshold value within the plurality of threshold
values; and [0172] wherein the selection of the second sampling
rate is based upon both the correspondence of the first value of
acceleration data to the first threshold value and the classified
activity category. [0173] A3. The non-transitory computer-readable
medium of Para A2, wherein determining the first sampling rate
corresponds to the threshold value occurs at a sampling rate
processor located on the device and is based upon at least one of:
the first value of the acceleration data is equal to the threshold
value, the first value of the acceleration data is numerically
closer to, and greater than, a second threshold value, the first
value of the acceleration data is within a range of the threshold
value. [0174] A4. The non-transitory computer-readable medium of
any preceding Para, wherein the first value of the acceleration
data comprises at least one of: an amplitude or a frequency. [0175]
A5. The non-transitory computer-readable medium of any preceding
Para, wherein the accelerometer is a first accelerometer and the
medium further comprises computer-executable instructions that when
executed further perform at least: [0176] selecting a second sensor
that is not the first accelerometer to capture motion data from the
user based upon at least one of: (a) the correspondence of the
first value of acceleration data to the first threshold value and
(b) the classified activity category. [0177] A6. The non-transitory
computer-readable medium of Para A5, wherein after selecting of the
second sensor, the first accelerometer and the second sensor are
utilized to capture the user's movement. [0178] A7. The
non-transitory computer-readable medium of Para A5, wherein after
selecting the second sensor, the first accelerometer is not used to
capture the user's movement. [0179] A8. The non-transitory
computer-readable medium of any preceding Para, wherein the
accelerometer is a first accelerometer and the medium further
comprises computer-executable instructions that when executed
further perform at least: [0180] comparing a value of acceleration
data obtained from the first accelerometer during its operation at
the first sampling rate to a plurality of threshold values; [0181]
determining that the value of acceleration data corresponds to a
first threshold value within the plurality the threshold values;
and [0182] based upon the correspondence to the first threshold
value and the classified activity category, selecting a second
threshold value. [0183] A9. The non-transitory computer-readable
medium of any preceding Para, wherein the sampling rate processor
determines the first sampling rate as a low-battery sampling rate
corresponding to a low level of stored electrical energy in the
power supply. [0184] A10. A non-transitory computer-readable medium
comprising computer-executable instructions that when executed by a
processor is configured to perform at least: [0185] a) receiving,
from an accelerometer of a processor, acceleration data; [0186] b)
identifying an activity from the received acceleration data; [0187]
c) adjusting a sampling rate of the accelerometer based on the
activity identified in b). [0188] A11. The non-transitory
computer-readable medium of Para A10, wherein the medium further
comprises computer-executable instructions that when executed
further perform at least: [0189] comparing a first value of
acceleration data obtained from the accelerometer during operation
against a plurality of threshold values; [0190] determining that
the first value of acceleration data corresponds to a first [0191]
threshold value within the plurality the threshold values; and
[0192] wherein the adjustment of the sampling rate is based upon
the correspondence of the first value of acceleration data to the
first threshold value. [0193] A12. The non-transitory
computer-readable medium of Para A10 or A11, wherein the
accelerometer is a first accelerometer and the medium further
comprises computer-executable instructions that when executed
further perform at least: [0194] selecting a second sensor that is
not the first accelerometer to capture motion data based upon the
correspondence of the first value of acceleration data to the first
threshold value. [0195] A13. The non-transitory computer-readable
medium of Para A12, wherein after selecting of the second sensor,
the first accelerometer and the second sensor are utilized to
capture motion data. [0196] A14. The non-transitory
computer-readable medium of Para A12, wherein after selecting of
the second sensor, the first accelerometer is not used to capture
motion data. [0197] A15. The non-transitory computer-readable
medium of any of Paras A10 to A14, wherein the accelerometer is a
first accelerometer and the medium further comprises
computer-executable instructions that when executed further perform
at least:
[0198] comparing a value of acceleration data obtained from the
first accelerometer during its operation at the first sampling rate
to a plurality of threshold values; [0199] determining that the
value of acceleration data corresponds to a first threshold value
within the plurality the threshold values; and [0200] based upon
the correspondence to the first threshold value and the classified
activity category, selecting a second threshold value. [0201] A16.
A unitary apparatus configured to be worn by a user, comprising:
[0202] a structure configured to be worn around an appendage of a
user, comprising: [0203] a power supply; [0204] a sensor configured
to capture acceleration data from the appendage of the user; [0205]
an analysis processor; [0206] a sampling rate processor; and [0207]
the non-transitory computer-readable medium of any preceding
Para.
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