U.S. patent application number 14/930452 was filed with the patent office on 2016-03-17 for automatic exercise segmentation and recognition.
The applicant listed for this patent is Microsoft Technology Licensing, LLC. Invention is credited to Andrew Guillory, Ilya Kelner, Daniel Morris, T. Scott Saponas, Farah Shariff, Dennis Tom.
Application Number | 20160074706 14/930452 |
Document ID | / |
Family ID | 50382624 |
Filed Date | 2016-03-17 |
United States Patent
Application |
20160074706 |
Kind Code |
A1 |
Morris; Daniel ; et
al. |
March 17, 2016 |
AUTOMATIC EXERCISE SEGMENTATION AND RECOGNITION
Abstract
A physical activity monitoring device includes a sensor array
with one or more sensors configured to measure physical activity
attributes of a user. A controller automatically determines time
intervals where the user is actively engaged in a physical activity
based on the physical activity attributes. The controller also
automatically determines a type of physical activity the user in
actively engaged in during the determined time intervals based on
the physical activity attributes. A reporter outputs information
regarding the type of physical activity to the user.
Inventors: |
Morris; Daniel; (Bellevue,
WA) ; Kelner; Ilya; (Redmond, WA) ; Shariff;
Farah; (Kirkland, WA) ; Tom; Dennis; (Redmond,
WA) ; Saponas; T. Scott; (Woodinville, WA) ;
Guillory; Andrew; (Seattle, WA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Microsoft Technology Licensing, LLC |
Redmond |
WA |
US |
|
|
Family ID: |
50382624 |
Appl. No.: |
14/930452 |
Filed: |
November 2, 2015 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
13785184 |
Mar 5, 2013 |
9174084 |
|
|
14930452 |
|
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|
Current U.S.
Class: |
434/247 |
Current CPC
Class: |
A63B 24/0062 20130101;
A61B 5/7264 20130101; A61B 5/1112 20130101; A63B 2071/0663
20130101; A61B 5/1118 20130101; A63B 24/00 20130101; A61B 5/1123
20130101; A61B 2562/0219 20130101; A61B 5/02438 20130101; A61B
5/6824 20130101; A63B 2220/40 20130101 |
International
Class: |
A63B 24/00 20060101
A63B024/00 |
Claims
1. A physical activity monitoring device, comprising: a sensor
array including one or more sensors configured to measure physical
activity attributes of a user while the user is wearing the
physical activity monitoring device; a controller operable to:
receive signal information from the sensor array; identify
predetermined signal characteristics in the signal information;
analyze the predetermined signal characteristics using a supervised
classifier trained to recognize if the user is actively engaged in
a physical activity; automatically determine time intervals where
the user is actively engaged in the physical activity using the
physical activity attributes; and automatically determine a type of
physical activity the user is actively engaged in during the
determined time intervals using the physical activity attributes;
and a reporter to output information regarding the type of physical
activity.
2. The monitoring device of claim 1, where the one or more sensors
include an accelerometer.
3. The monitoring device of claim 1, wherein the signal information
includes signals in three dimensions, and wherein the controller is
operable to dimensionally reduce signals in two of the three
dimensions into a signal in one dimension.
4. The monitoring device of claim 1, wherein the predetermined
signal characteristics include one or more of: a number of
autocorrelation peaks, a number of negative autocorrelation peaks,
a maximum autocorrelation value, a log of a maximum autocorrelation
value, a root-mean-square amplitude, a mean, a standard deviation,
a variance, or an integrated root-mean-square amplitude.
5. The monitoring device of claim 1, wherein the supervised
classifier includes a support vector machine, and wherein to
analyze the predetermined signal characteristics the controller is
operable to: train the support vector machine with data collected
from a plurality of users during time intervals where the users
were actively engaged in a physical activity and time intervals
where the users were not actively engaged in a physical activity;
generate a set of transformation vectors, a weight vector and a
threshold representative of the user actively engaged in a physical
activity; multiply the predetermined signal characteristics by the
set of transformation vectors and weight vector to obtain a
plurality of multiplication products; compare the multiplication
products to the threshold; classify a value above the threshold as
representative of a time interval wherein the user is actively
engaged in a physical activity; classify a value below the
threshold as representative of a time interval wherein the user is
not actively engaged in a physical activity; and classify time
intervals where the user is likely to be actively engaged in a
physical activity based on the classified values.
6. The monitoring device of claim 5, further comprising an
aggregator configured to determine a time interval where the user
is likely to be actively engaged in a physical activity.
7. The monitoring device of claim 6, wherein the controller is
operable to receive signal information from the sensor array and
further operable to receive a set of time intervals from the
aggregator, and wherein to determine a type of physical activity
the user is actively engaged in during the set of time intervals,
the controller is operable to: identify predetermined signal
characteristics for the signal information; and analyze the
predetermined signal characteristics for the signal information
using a supervised classifier trained to recognize the type of
physical activity the user is actively engaged in during the set of
time intervals.
8. The monitoring device of claim 7, where the supervised
classifier includes a support vector machine, and wherein to
analyze the predetermined signal characteristics, the controller is
further operable to: train the support vector machine with data
collected from a plurality of users during time intervals where the
users were engaged in a plurality of types of physical activity;
generate a set of transformation vectors and a weight vector
representative of a user engaged in a type of physical activity;
multiply the predetermined signal characteristics by the set of
transformation vectors and weight vector to obtain a plurality of
multiplication products; compare the multiplication products to
data sets representative of each of a plurality of predetermined
activities where the data sets have been predetermined through
machine learning; and classify time intervals as representative of
a type of physical activity.
9. The monitoring device of claim 7, further comprising a voting
machine configured to determine the type of physical activity the
user is likely to be actively engaged in during a time interval
where the aggregator has determined that the user is engaged in a
physical activity.
10. The monitoring device of claim 1, wherein the controller is
further operable to determine a number of repetitions the user
performs of a repetitive physical activity.
11. The monitoring device of claim 10, wherein the controller is
operable to determine the number of repetitions through a counting
method that includes: receiving a signal from the sensor array; and
transforming the signal into a dimensionally reduced signal having
at least one fewer dimensions than the signal received from the
sensor array; counting a number of peaks of the dimensionally
reduced signal; and outputting the number of peaks.
12. The monitoring device of claim 11, wherein the counting method
further includes: determining a set of candidate peaks; filtering
the set of candidate peaks using local period estimates; filtering
the set of candidate peaks using amplitude statistics; and counting
a number of peaks from the set of candidate peaks.
13. The monitoring device of claim 12, wherein the counting method
further includes: determining a set of candidate valleys; filtering
the set of candidate valleys using local period estimates;
filtering the set of candidate valleys using amplitude statistics;
counting a number of valleys from the set of candidate valleys;
comparing the number of valleys to the number of peaks; and
designating the greater of the number of valleys and the number of
peaks as a number of repetitions; and outputting the number of
repetitions.
14. A method of monitoring physical activity, comprising:
measuring, with a sensor array including one or more sensors,
physical activity attributes of a user wearing a physical activity
monitoring device including the one or more sensors; automatically
determining a set of time intervals where the user is actively
engaged in a physical activity based on the physical activity
attributes by: receiving signal information from the sensor array;
identifying predetermined signal characteristics for the signal
information; and analyzing the predetermined signal characteristics
for the signal information using a supervised classifier; using the
supervised classifier to automatically determine a type of physical
activity the user is actively engaged in based on the physical
activity attributes; and outputting information regarding the type
of physical activity.
15. The method of claim 14, further comprising outputting
information regarding a form of the user performing the physical
activity.
16. The method of claim 14, where the supervised classifier
includes a support vector machine, and where analyzing the
predetermined signal characteristics further includes: training the
support vector machine with data collected from a plurality of
users during time intervals where the users were engaged in a
plurality of types of physical activity; generating a set of
transformation vectors and a weight vector representative of a user
engaged in a type of physical activity; multiplying the
predetermined signal characteristics by the set of transformation
vectors and weight vector to obtain a plurality of multiplication
products; comparing the multiplication products to data sets
representative of each of a plurality of predetermined activities
where the data sets have been predetermined through machine
learning; and classifying time intervals as representative of a
type of physical activity.
17. The method of claim 14, further comprising determining the type
of physical activity the user is likely to be actively engaged in
during a time interval where an aggregator has determined that the
user is engaged in a physical activity.
18. The method of claim 14, where the one or more sensors include
an accelerometer.
19. A physical activity monitoring device, comprising: a sensor
array including an accelerometer configured to measure physical
activity attributes of a user wearing the physical activity
monitoring device; a controller operable to receive acceleration
signal information from the sensor array and to automatically
determine time intervals where the user is actively engaged in a
physical activity by: identifying predetermined acceleration
characteristics for each time interval; and analyzing the
predetermined acceleration characteristics for each time interval
using a supervised classifier trained to recognize if the user is
actively engaged in the physical activity during the time interval;
the controller further operable to automatically determine a type
of physical activity the user is actively engaged in during the
determined time intervals using the physical activity attributes
corresponding to the determined time intervals; and a reporter to
output information regarding the type of physical activity.
20. The physical activity monitoring device of claim 19, wherein
the signal information includes signals in three dimensions, and
wherein the controller is operable to dimensionally reduce signals
in two of the three dimensions into a signal in one dimension.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This application is a continuation of U.S. patent
application Ser. No. 13/785,184, filed Mar. 5, 2013 entitled
"AUTOMATIC EXERCISE SEGMENTATION AND RECOGNITION", the entire
contents of which are hereby incorporated by reference for all
purposes.
BACKGROUND
[0002] Exercise and other physical activities can be very
beneficial to a person's health and wellbeing. Some people utilize
personal trainers to enhance health and wellbeing through
structured exercise regimens. However, not everybody can afford a
personal trainer. Even people that frequently train with a personal
trainer are unlikely to have access to the personal trainer at all
times when physical activities are performed.
SUMMARY
[0003] A physical activity monitoring device includes a sensor
array with one or more sensors configured to measure physical
activity attributes of a user. A controller automatically
determines time intervals where the user is actively engaged in a
physical activity based on the physical activity attributes. The
controller also automatically determines a type of physical
activity the user in actively engaged in during the determined time
intervals based on the physical activity attributes. A reporter
outputs information regarding the type of physical activity to the
user.
[0004] This Summary is provided to introduce a selection of
concepts in a simplified form that are further described below in
the Detailed Description. This 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. Furthermore, the claimed subject matter is not
limited to implementations that solve any or all disadvantages
noted in any part of this disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] FIG. 1 shows a user wearing a physical activity monitoring
device in accordance with an embodiment of the present
disclosure.
[0006] FIG. 2 shows a physical activity monitoring device in
accordance with an embodiment of the present disclosure.
[0007] FIG. 3 shows an example method of determining periods in
which a user is actively engaged in a physical activity in
accordance with an embodiment of the present disclosure.
[0008] FIG. 4 shows analysis of an example segment of a workout
regimen in accordance with an embodiment of the present
disclosure.
[0009] FIG. 5 shows an example method of recognizing a type of
physical activity in accordance with an embodiment of the present
disclosure.
[0010] FIG. 6 shows an example method of counting exercise
repetitions in accordance with an embodiment of the present
disclosure.
[0011] FIG. 7 shows an example method of augmenting a game
experience in accordance with an embodiment of the present
disclosure.
[0012] FIG. 8 shows an example method of physically training a user
in accordance with an embodiment of the present disclosure.
DETAILED DESCRIPTION
[0013] This disclosure is directed to a physical activity
monitoring device (PAMD) that may be used to augment a gaming
experience and/or to receive and monitor a workout regimen.
[0014] In a workout, an individual may spend time performing
physical activities or exercises as well as time spent recovering
from the activities, time spent preparing for the activities, and
time spent performing non-exercise activity, such as drinking
water. In order for the PAMD to recognize and distinguish physical
activities or exercises performed by a user wearing the PAMD, it
may first distinguish between times of actual exercise and
non-exercise activities. In some embodiments, the PAMD samples user
movements with a sensor array including one or more sensors
configured to measure physical activity attributes of a user
wearing the PAMD. The sampled data may then be segmented into
periods of exercise and periods of non-exercise. Within the periods
of exercise, the PAMD may then be trained to uniquely identify
patterns in the sampled data that are representative of specific
physical activities or exercises, thereby recognizing the physical
activity or exercise performed by the user wearing the PAMD. For
repetitive physical activities or exercises, the PAMD may then
count the number of repetitions of the given repetitive physical
activity or exercise performed by the user wearing the PAMD.
[0015] FIG. 1 shows a user 101 wearing a physical activity
monitoring device 102 in accordance with the present disclosure. In
some embodiments, PAMD 102 may be in the form of a wearable arm
band. PAMD 102 may be worn by user 101 while user 101 is exercising
or otherwise performing a specific physical activity. PAMD 102 may
employ a method of smart activity analysis. Smart activity analysis
may allow for the PAMD to recognize and distinguish physical
activities or exercises performed by a user wearing the PAMD, and
further may allow for the PAMD to count the repetitions of a
physical activity or exercise performed by a user wearing the
PAMD.
[0016] FIG. 2 schematically shows a physical activity monitoring
device 200 in accordance with the present disclosure. PAMD 200 may
include sensor array 210, logic machine 215, storage machine 217,
controller 220, supervised classifier 230a, supervised classifier
230b, aggregator 240, communication subsystem 250, reporter 260,
voting machine 270 and display subsystem 280.
[0017] Sensor array 210 may include one or more sensors, such as
accelerometer 214, GPS 212, gyroscope 216, heart rate monitor 218,
and/or other suitable sensors. Data from sensor array 210 may allow
the device to automatically distinguish between different physical
activities or exercises performed by the user wearing the PAMD. In
some examples, accelerometer 214 may sample user movements at a
fixed sampling rate. The sampling may occur at a sample rate of 25
Hz, or another suitable sampling rate, such as 50 Hz. Data from GPS
212 may be used to learn the speed of the user performing a
physical activity or exercise outdoors. For example, the GPS
derived data may be used to distinguish between periods of running,
walking and biking. Furthermore, the GPS may be used to assess a
user's location. Such location information may be used to
distinguish activities. For example, a user is likely to be playing
tennis when on a tennis court or golfing when on a golf course. In
concert with data received from the accelerometer, the GPS data may
be used to calibrate the user's stride. This information may be
stored and used to distinguish and calibrate the same physical
activities or exercises when performed indoors or when GPS data is
otherwise unavailable, allowing for accurate distance and pace
information to be calculated in the absence of GPS data.
[0018] Accelerometer 214 optionally may be a 3-axis accelerometer.
Data received from accelerometer 214 may be used to detect
variations in patterns for different repetitive physical activities
or exercises that may include pushups, situps, squats, etc. The
repeatability of the signals for such repetitive physical
activities or exercises may be used to detect the repetitions of
these and similar exercises. Data received from accelerometer 214
may also be used to detect variations in patterns for different
static physical activities or exercises that may include wall
squats, planks, yoga poses, etc.
[0019] For distance based activities, PAMD 200 may further detect
and distinguish between varying speeds of movement, including
walking, jogging, fast jogging, and running. This may be
accomplished by measuring the speed of the user's footfall with
accelerometer 214. This data may then be used to dynamically adjust
the distance calculation specific to the activity. For example, the
number of steps taken by a user while walking may equate to a
shorter distance than for the same number of steps taken by the
same user while running. Over numerous periods of physical activity
or exercise, the user's true stride length for walking, jogging,
running, etc. may be learned using data from GPS 212 and
accelerometer 214.
[0020] PAMD 200 may further include controller 220, which
optionally may be instantiated via use of logic machine 215 and
storage machine 217. Controller 220 may be configured to
automatically determine time intervals where the user is actively
engaged in a physical activity based on the physical activity
attributes. This process may also be referred to herein as
segmentation. In one example, PAMD 200 may indicate that the user
is to perform jumping jacks as an exercise. Following this
indication, the user may take time to prepare for the exercise, for
example walking around a room, taking a drink of water, or getting
in position to begin the jumping jacks. The process of segmentation
may separate periods of actual exercise from other activities not
related to the physical activity or exercise. Removing periods of
non-exercise from the analysis may lessen the possibility of PAMD
200 counting false repetitions. Similarly, PAMD 200 may be able to
more accurately determine which physical activity or exercise a
user is engaged in if the recognition system only analyzes data
from periods of time when the user is actively engaged in the
physical activity or exercise.
[0021] In one example, PAMD 200 may indicate that a user is to
perform a time based activity, for example pushups for 30 seconds.
PAMD 200 may more accurately track the 30 seconds of pushups if it
does not begin evaluating the user's physical activity attributes
during the time the user is getting into position to perform the
pushups. By accurately determining when the user is performing
pushups, PAMD 200 may be able to make accurate claims about user
biometrics, for example, how many calories the user burned while
performing the pushups.
[0022] PAMD 200 may be trained through machine learning to
recognize periods where the user is actively engaged in a physical
activity or exercise. This process of segmentation may be further
broken down into sub-processes of preprocessing, feature
computation, classification, and aggregation.
[0023] FIG. 3 shows an example method 300 that may be used by
controller 220 to determine periods where the user is actively
engaged in a physical activity. At 302, method 300 includes
receiving signal information from a sensor array (e.g., sensor
array 210). Such signal information may be received by controller
220, for example. The signal information is representative of the
physical activity attributes of the user. In some examples, this
signal information may include raw data from accelerometer 214 or
gyroscope 216. Accelerometer 214 and gyroscope 216 may each output
three raw signals, giving a total of six raw input signals. The raw
input signals may then be run through a low-pass filter, the output
of which may then be six smoothed signals.
[0024] At 304, method 300 may include determining time intervals
where the user is actively engaged in a physical activity. For
example, controller 220 may use the signal information received at
302 as the basis for determining time intervals. As shown in FIG.
3, the process of determining time intervals may include a
plurality of sub-processes. Nonlimiting examples of such
sub-processes are provided below. However, it should be understood
that time intervals of active engagement may be determined in any
suitable manner without departing from the scope of this
disclosure.
[0025] At 306, method 300 may include dividing the signal
information into overlapping segments. In one example, the data may
be divided into windows with a length of five seconds. Each window
may be advanced by 200 ms from the previous window, such that each
five second window shares 4.8 seconds of data with the previous and
following windows.
[0026] At 308, method 300 may include identifying predetermined
signal characteristics (e.g., acceleration characteristics of
acceleration information measured by an accelerometer of the sensor
array) for each overlapping segment. In some examples, controller
220 may transform each five second window of smoothed data into 200
signal characteristics that are then used to characterize the
physical activity or exercise, but there may be fewer or greater
signal characteristics. In one example, the six smoothed signals
may be transformed into ten output signals. The ten output signals
in this example may include smoothed accelerometer data in the x, y
and z axes, smoothed gyroscope data in the x, y and z axes, the
magnitude of the accelerometer signal at each sample, the magnitude
of the gyroscope signal at each sample, the projection of the
three-dimensional accelerometer signal onto the first principal
component of that signal, projection of accelerometer signals in
the y and z axes onto their own principal component, and the
projection of the three-dimensional gyroscope signal onto the first
principal component of that signal.
[0027] The output signals may be selected based on characteristics
of the PAMD. In some embodiments where the signal information
includes signals in three dimensions, signals in two of the three
dimensions may be dimensionally reduced into a signal in one
dimension. For example, if the PAMD is configured as a device that
adheres to the body of the user or otherwise is configured to be
fixed to the body of the user, the smoothed data in the x, y and z
axes may be considered to accurately reflect the movement of the
user in these three axes. However, if the PAMD is configured as a
wearable arm-band, for example, the PAMD may be subject to rotation
about the arm or wrist of the user. In this example, data in the
x-axis (e.g. along the arm of the user) may be considered to
accurately reflect the movement of the user in the x-axis, but data
in the y and z axes could represent movement in a plurality of
axes, depending on the position of the PAMD. In this example, the
projection of accelerometer signals in the y and z axes on their
own principal components may be selected as output signals. By
reducing the dimensionality of signals in the y and z axes,
rotation of the PAMD about the arm of the user may be accounted for
by compressing the unknown axes into a more predictable signal.
[0028] For each of these output signals, controller 220 may then
calculate 20 signal characteristics for each output signal.
Controller 220 may also calculate a greater or lesser number of
signal characteristics. In some examples, the signal
characteristics may include a number of autocorrelation peaks, a
number of negative autocorrelation peaks, a maximum autocorrelation
value, a log of a maximum autocorrelation value, a root-mean-square
amplitude, a mean, a standard deviation, a variance, and an
integrated root-mean-square amplitude. The signal characteristics
may also include a number of strong peaks, where strong peaks may
be defined as the number of autocorrelation peaks that are larger
than their neighboring peaks by a threshold and are more than a
threshold lag away from their neighboring peaks. The signal
characteristics may also include a number of weak peaks, where weak
peaks may be defined as the number of autocorrelation peaks that
are within a threshold height of their neighboring peaks and are
less than a threshold lag away from their neighboring peaks. The
signal characteristics may further include the value of the first
autocorrelation peak after a zero-crossing, local non-linearity or
other measures of how well a best-fit line explains the data, and a
set of power bands, where a magnitude is calculated for the power
spectrum in each selected band spread over the range of frequencies
that can be obtained by the sensor array. In this example, seven
power bands may be calculated, but there may be more or fewer power
bands, for example ten power bands.
[0029] Additional signal characteristics may be calculated for each
signal. For each five second window, the signal characteristics may
give an indication of the autocorrelation of the signal, in other
words, an indication of how repetitive or self-similar the data is.
Signals from time periods where the user is exercising may be more
repetitive than signals from time periods where the user is not
exercising. In some examples, signals from time periods where the
user is engaged in fast motions may be more likely to correspond to
exercise than non-exercise.
[0030] At 310, method 300 may include analyzing the predetermined
signal characteristics for each overlapping segment using a
supervised classifier. As shown in FIG. 2, controller 220 may
include supervised classifier 230a. Supervised classifier 230a may
be trained to recognize if the user is actively engaged in a
physical activity during the overlapping segment. In general,
supervised classifier 230a and/or other aspects of controller 220
may be trained through a machine-learning process to recognize
signal characteristics that are representative of a user being
actively engaged in a physical activity or exercise and to further
recognize signal characteristics that are representative of a user
not being actively engaged in a physical activity or exercise.
[0031] In one example, supervised classifier 230a utilizes a
support vector machine (SVM), for example a linear support vector
machine. In some examples supervised classifier 230a utilizes a
machine-learning decision tree. In some examples where supervised
classifier 230a utilizes an SVM, the SVM may be configured to
generate a vector of numbers, and further configured to multiply
the predetermined signal characteristics by the vector of numbers
to obtain a plurality of multiplication products. The SVM may be
further configured to compare the multiplication products to a
threshold or thresholds determined through machine learning as
described above. The SVM may then be configured to classify a value
above the threshold as representative of an overlapping segment
wherein the user is actively engaged in a physical activity and
classify a value below the threshold as representative of an
overlapping segment wherein the user is not actively engaged in a
physical activity.
[0032] In some examples where supervised classifier 230a includes
an SVM, analyzing the predetermined signal characteristics may
include training the support vector machine with data collected
from a plurality of users during time intervals where the users or
training subjects were actively engaged in a physical activity or
exercise and time intervals where the users or training subjects
were not actively engaged in a physical activity or exercise.
Analyzing the predetermined signal characteristics may further
include generating a set of transformation vectors, a weight vector
and a threshold representative of a user actively engaged in a
physical activity or exercise, followed by multiplying the
predetermined signal characteristics by the set of transformation
vectors and weight vector to obtain a plurality of multiplication
products. Analyzing the predetermined signal characteristics may
further include comparing the multiplication products to the
threshold, classifying a value above the threshold as
representative of an overlapping segment wherein the user is
actively engaged in a physical activity or exercise; and
classifying a value below the threshold as representative of an
overlapping segment wherein the user is not actively engaged in a
physical activity or exercise.
[0033] When supervised classifier 230a has classified each
overlapping segment, PAMD 200 may formulate a single best guess as
to whether the user is actively engaged in a physical activity or
exercise during each 5-second window.
[0034] As shown in FIG. 2, PAMD 200 may further include aggregator
240 configured to determine a time interval defined by a plurality
of the classified overlapping segments where the user is likely to
be actively engaged in a physical activity. In other words,
aggregator 240 may improve accuracy of supervised classifier 230a,
or analyze groups of overlapping segments to determine longer time
intervals that are representative of exercise or non-exercise.
[0035] Predictions made by supervised classifier 230a may
occasionally flip back and forth. In one example, a user may be
standing still for a period of time, and some overlapping segments
may be classified as representative of the user being engaged in a
physical activity or exercise. The reverse may also take place--a
user may be exercising for a period of time and some overlapping
segments within that period may be classified as representative of
non-exercise. In some examples, a user may pause to catch their
breath in the middle of an exercise; however, overlapping segments
on either side of the pause are actually representative of the same
physical activity or exercise. In an example where the user pauses
to catch their breath in the middle of a repetitive activity,
repetitions on both sides of the pause should be counted towards
the same total number of repetitions. In this manner, aggregator
240 may fix the output of the classifier to output useable time
intervals where the user is engaged in a physical activity or
exercise.
[0036] An example of aggregator 240 fixing the output of supervised
classifier 230a is shown in FIG. 4. FIG. 4 shows an example of a
segment of physical activity that may be part of a workout regimen.
Graph 401 shows the output of supervised classifier 230a for a time
interval spanning approximately 1 minute. Each plot point 405
represents one overlapping segment. Each overlapping segment may be
given a value of 1 (classified as "exercise") or 0 (classified as
"non-exercise"). Graph 402 shows the actual activity of a user. As
shown at 410, the user performed sit ups for a period of 20
seconds. However, as shown at 415, some overlapping segments may be
initially classified as "exercise", even when the user was not
actively engaged in a physical activity or exercise. Similarly, as
shown at 420, some overlapping segments may be classified as
"non-exercise", even when the user was actively engaged in a
physical activity or exercise. As shown in graph 403, aggregator
240 may fix the output of the classifier to output useable time
intervals where the user is engaged in a physical activity or
exercise, such as time interval 430.
[0037] Aggregator 240 may employ one or more methods to analyze the
classification of overlapping segments and output a time interval
where the user is likely to be engaged in a physical activity or
exercise. In one example, aggregator 240 uses a process of
streak-based aggregation. In this example, aggregator 240 starts by
assuming a non-exercise state and proceeds to analyze the
overlapping segments in sequence. If a predetermined number
(k.sub.1) of overlapping segments are classified as "exercise",
aggregator 240 may switch to assuming an exercise state, beginning
from the first of the k.sub.1 overlapping segments. In one example,
k.sub.1 may be set to fifteen overlapping segments. In other words,
if fifteen consecutive overlapping segments are classified as
"exercise", aggregator 240 switches to assume an exercise state.
Another predetermined number (k.sub.2) which may or may not be
equal to k.sub.1 may be utilized to indicate aggregator 240 should
switch from assuming an exercise state to assuming a non-exercise
state.
[0038] In some examples, aggregator 240 uses a process of
percent-based aggregation. In this example, aggregator 240 starts
by assuming a non-exercise state and analyzes the overlapping
segments in sequence. If a predetermined percentage (p.sub.1%) of
overlapping segments is classified as "exercise" over a
predetermined time interval (t), aggregator 240 may switch to
assuming an exercise state, beginning from the first of the
overlapping segments classified as "exercise". For example, p.sub.1
may equal 75 and t may be equal to 10, thus when 75% of overlapping
segments over 10 seconds are classified as "exercise", aggregator
240 may switch to assuming an exercise state, beginning from the
first of the overlapping segments classified as "exercise". Another
predetermined percentage (p.sub.2%) which may or may not be equal
to p.sub.1% may be utilized to indicate aggregator 240 should
switch from assuming an exercise state to assuming a non-exercise
state.
[0039] In some examples, aggregator 240 uses a process of
accumulator-based aggregation. In this example, aggregator 240
starts by assuming a non-exercise state and analyzes the
overlapping segments in sequence. Each overlapping segment that is
classified as "exercise" adds a point to a subtotal. When the
subtotal exceeds a predetermined threshold (a.sub.1) aggregator 240
switches to assume an exercise state. Another predetermined
threshold (a.sub.2) which may or may not be equal to (a.sub.1) may
be utilized to indicate aggregator 240 should switch from assuming
an exercise state to assuming a non-exercise state.
[0040] Aggregator 240 may use one of these processes or other
similar processes or a combination of multiple processes to analyze
data from supervised classifier 230a and output time intervals
where the user is engaged in a physical activity or exercise. For
example, aggregator 240 may use a process of streak-based
aggregation to determine the starting point of a physical activity
or exercise, and then may further use a process of
accumulator-based aggregation in determining whether the user has
stopped performing the physical activity or exercise. In some
embodiments, aggregator 240 may run multiple aggregators
concurrently (for example, streak-based and accumulator-based
aggregators) and identify the start or end of physical activity or
exercise when any one of the multiple aggregators indicates that
physical activity or exercise starts or ends.
[0041] In some examples, initial data from the sensor array may be
re-analyzed by supervised classifier 230a as described above,
further using the output of aggregator 240 as an input to machine
learning algorithms. The predetermined constants may be shared or
uniquely assigned for different exercises or different types of
exercises (for example, repetitive exercises such as pushups may
use different constants than non-repetitive exercises such as
jogging).
[0042] Controller 220 may also be configured to automatically
determine a type of physical activity the user is actively engaged
in during the determined time intervals based on the physical
activity attributes. In other words, controller 220 may determine
which exercise the user is actively engaged in each time interval
where it has been determined the user is engaged in a physical
activity or exercise. This process may be referred to herein as
recognition. The process of recognition optionally may follow the
process of aggregation and/or some recognition may be performed in
parallel with segmentation and/or aggregation. In some embodiments,
controller 220 may be configured to automatically determine a type
of physical activity the user is actively engaged in during the
determined time intervals based on only the physical activity
attributes corresponding to the determined time intervals. In other
words, only the physical activity attributes received from sensor
array 210 for periods of time that have been determined as periods
of exercise are used to determine the type of physical
activity.
[0043] Recognition of the type of exercise may be used in
downstream applications including counting of exercise repetitions,
computing the efficiency or power of an exercise, determining a
user's caloric expenditure over the course of an exercise, etc.
PAMD 200 may also be configured to analyze a user's physical
activity attributes and further provide feedback to the user
regarding the user's form or other information that may enhance the
user's exercise experience. Controller 220 may be trained to
recognize types of physical activities based on the user's physical
activity attributes through a machine learning process. In some
examples, PAMD 200 may indicate a specific activity to be performed
by the user. In these examples, the process of recognition may be
ignored, combined with, and/or reinforced by the process of
segmentation.
[0044] FIG. 5 shows one example method 500 for recognition of a
type of physical activity or exercise. At 502, method 500 may
include receiving signal information from the sensor array (e.g.,
sensor array 210). Such signal information may be received by
controller 220, for example. The signal information may include raw
data from accelerometer 214 and gyroscope 216. The raw data may be
further processed by a low-pass filter to yield smoothed
accelerometer and gyroscope data. The signal information may also
include the projection of the accelerometer signal onto the first
principal component of that signal. The signal information may also
include the projection of the gyroscope signal onto the first
principal component of that signal.
[0045] At 504, method 500 may include receiving a set of time
intervals from the aggregator (e.g. aggregator 240). Such a set of
time intervals may be received by controller 220 and may be
representative of time intervals in which the user is engaged in a
physical activity or exercise. At 506, method 500 may include
determining a type of physical activity the user is actively
engaged in during the set of time intervals (e.g. recognition). In
other words, the process of recognition may be applied when
aggregator 240 indicates that the user is engaged in a physical
activity or exercise.
[0046] The process of recognition may include a plurality of
sub-processes. Nonlimiting examples of such sub-processes are
provided below. However, it should be understood that recognition
may be performed in any suitable manner without departing from the
scope of this disclosure.
[0047] At 508, method 500 may include dividing the signal
information into overlapping segments. In one example, the data may
be divided into windows with a length of five seconds. Each window
may be advanced by 200 ms from the previous window, such that each
five second window shares 4.8 seconds of data with the previous and
following windows.
[0048] At 510, method 500 may include identifying predetermined
signal characteristics for each overlapping segment. In one
example, each five second window of smoothed data may be
transformed into a plurality of signal characteristics that are
then used to characterize the physical activity or exercise. As one
nonlimiting example, 60 signal characteristics may be used. For
example, controller 220 may calculate 20 signal characteristics for
each of the three axes of each of the overlapping segments. In this
example, the signal characteristics include five evenly spaced
autocorrelation bins, the root-mean-square amplitude, ten evenly
spaced power bands, mean, standard deviation, kurtosis and
interquartile range. Other signal characteristics may also be
calculated for each of the three axes.
[0049] At 512, method 500 may include analyzing the predetermined
signal characteristics for each overlapping segment using a
supervised classifier (e.g. supervised classifier 230b). Supervised
classifier 230b may be trained to recognize the type of physical
activity the user is actively engaged in during the overlapping
segment. Supervised classifier 230b may be trained through a
process of machine learning to recognize signal characteristics
that are representative of a particular type of physical activity
or exercise.
[0050] In some examples, supervised classifier 230b may utilize a
support vector machine (SVM) and/or a decision tree, as described
above with reference to supervised classifier 230a. For example,
analyzing the predetermined signal characteristics may include
training the support vector machine with data collected from a
plurality of users during time intervals where the users were
engaged in a plurality of types of physical activity or exercise
and generating a set of transformation vectors and a weight vector
representative of a user engaged in a type of physical activity or
exercise. Analyzing the predetermined signal characteristics may
further include multiplying the predetermined signal
characteristics by the set of transformation vectors and weight
vector to obtain a plurality of multiplication products, comparing
the multiplication products to data sets representative of each of
a plurality of predetermined activities where the data sets have
been predetermined through machine learning, and classifying
overlapping segments as representative of a type of physical
activity.
[0051] As shown in FIG. 2, PAMD 200 may further include voting
machine 270. Voting machine 270 may be configured to determine the
type of physical activity the user is likely to be actively engaged
in during a time interval where aggregator 240 has determined that
the user is engaged in a physical activity.
[0052] As discussed above with regard to segmentation, for a given
time interval comprising a plurality of overlapping segments, the
supervised classifier may output several predictions which disagree
about which physical activity or exercise is being performed by the
user. A voting scheme may be implemented by voting machine 270 to
determine which physical activity or exercise the user is most
likely engaged in. Voting machine 270 may output a physical
activity or exercise for a given time interval, which may be
reported to the user and, in the case of a repetitive exercise,
which physical activity or exercise to use for counting.
[0053] In one example, voting machine 270 reports the output from a
single window starting two seconds into a time interval where
aggregator 240 has determined that the user is engaged in a
physical activity. The physical activity attributes of the user at
the beginning of an exercise period may be unreliable as the user
may still be getting into proper form. Similarly, the physical
activity of the user at the end of an exercise period may be
unreliable as the user may be slowing down or deviating from proper
form. In some examples, voting machine 270 may use a true voting
scheme similar to the aggregating process described above. In some
examples, voting machine 270 may alter its output during an
exercise period if strong evidence is presented that the user is
engaged in a different physical activity or exercise than initially
reported.
[0054] At the end of the recognition voting stage, each time
interval where aggregator 240 has determined that the user is
engaged in a physical activity may be classified as representative
of a type of physical activity or exercise. This information may be
delivered to reporter 260 to output information regarding the type
of physical activity.
[0055] PAMD 200 may be further configured to determine a number of
repetitions the user performs of a repetitive physical activity or
exercise. In other words, PAMD 200 may count the number of
repetitions of an exercise performed by the user. This may allow
for automatic tracking of activities for the user to review after
completing a workout regimen. This may also allow for real-time
goal assessment, where PAMD 200 may indicate the completion of a
target number of repetitions. Counting may be implemented through a
method including dimensionality reduction and peak-finding.
[0056] In one example, the counting process may assume that
segmentation and recognition have already been performed by
controller 220. The counting process may assume both start and end
times of a physical activity or exercise have been determined. The
process may also assume that the inputs include the classification
of a time interval and the raw accelerometer sensor data between
the start and end times. There may be types of physical activities
or exercises where gyroscopic sensor data optionally may be used to
improve the accuracy of the counting data. In some examples, the
counting process may occur while the user is actively performing
repetitions of a repetitive physical activity or exercise. In one
example, the methods described herein may be utilized repeatedly on
successively larger time intervals. In some examples, intermediate
counting results may be used to reduce the number of counting
iterations.
[0057] FIG. 6 shows an example method 600 for counting the
repetitions of a repetitive physical activity or exercise. At 602,
method 600 may include receiving a signal from a sensor array (e.g.
sensor array 210). Such signal information may be received by
controller 220, for example. At 604, method 600 may include
transforming the signal into a dimensionally reduced signal having
at least one fewer dimensions than the signal received from the
sensor array. At 706, method 700 may include counting a number of
peaks of the dimensionally reduced signal. At 708, method 700 may
include outputting the number of peaks.
[0058] In one example, the counting method may be divided into two
stages. In the first stage, the raw sensor data is received by
controller 220 from sensor array 210 and processed into a smooth
one-dimensional signal. The processing may be such that the
one-dimensional signal contains roughly the same number of peaks or
cycles as the number of repetitions of a physical activity or
exercise. The second stage includes counting the number of peaks of
the one-dimensional signal. This peak count may be output as the
repetition count.
[0059] The processing of the raw sensor data into a smooth
one-dimensional signal may be referred to herein as the signal
computation stage. The signal computation stage may include
applying a bandpass filter to the raw data. The bandpass filter may
be used to remove high frequency sensor noise as well as low
frequency changes in the signal, such as changes due to the
constant acceleration of gravity, for example. The bandpass filter
may output frequency content relevant to counting repetitions.
[0060] The signal computation stage may further comprise
subtracting the mean from the data. This may remove any remaining
constant bias in the signal remaining after the bandpass
filtering.
[0061] The signal computation stage may further comprise applying
principal component analysis (PCA) to the filtered data. The
implementation of PCA herein is similar to the application of PCA
for segmentation described above, but for the counting process, the
PCA is computed for the entire duration of a physical activity or
exercise, as opposed to a fixed-sized overlapping segment. The
smooth one-dimensional signal may be the result of the filtered
data being projected onto the first principal component found by
PCA.
[0062] In the example where PAMD 200 is a device worn on the wrist
of the user (as shown in FIG. 1), the use of PCA may take advantage
of the fact that for most exercises, most of the arm motion of the
user can be summarized by motion along a single axis. For a
shoulder press, for example, the axis would be the vertical or
up-and-down axis. By projecting the signal data onto this axis, the
dimensionality of the signal received from accelerometer 214 is
reduced from three dimensions to one dimension. The PCA projection
may now be such that a signal peak roughly corresponds to a
repetition.
[0063] The counting method of this example may next proceed to the
counting stage. In some examples, each repetition will correspond
to a single strong peak, where each of the peaks are of similar
shape and amplitude, and occur at a relatively constant frequency.
In these examples, counting the strong peaks may be accomplished
with any number of standard signal processing techniques. However,
not every signal will fit these parameters. There may be multiple
peaks per repetition, there may be large variation in the shape or
amplitude, and the peaks may occur at an erratic frequency.
[0064] A heuristic peak counting method may thus be used in order
to increase counting accuracy across a broad range of signal data.
In one example the method proceeds in multiple stages. In one
example, the method includes determining a set of candidate peaks,
filtering the set of candidate peaks using local period estimates,
filtering the set of candidate peaks using amplitude statistics,
counting the number of peaks from the set of candidate peaks and
outputting the number of peaks.
[0065] In one example, the method utilizes estimates of the minimum
and maximum time needed to perform one repetition of the exercise.
These values may be referred to herein as minAllowedPeriod and
maxAllowedPeriod. These values may be estimated from data on an
exercise-specific basis. For example, the values for jumping jacks
are different than the values for push-ups, because people tend to
do jumping jacks at a different rate than push-ups.
[0066] The counting method may begin with the computation of a set
of candidate peaks. The final set of peaks counted will be a subset
of this set of peaks. To compute the candidate peaks, the local
maxima in the signal may be determined. These local maxima may then
be sorted based on amplitude. A local maximum may be accepted as a
candidate peak so long as it is at least minAllowedPeriod seconds
away from a closest already accepted candidate peak. If two peaks
in the signal are very close together (e.g. only 200 ms apart), one
of them may not be a "real" repetition of the exercise. This
closeness threshold may be set based on the fastest reasonable
speed a human can perform a given exercise.
[0067] Counting peaks with a minimum separation may be done with a
standard signal-processing operation, such as the Matlab findpeaks
function with the MINPEAKDISTANCE parameter set to minAllowedPeriod
or other equivalent signal processing operation. In this example,
these candidate peaks are the input into the next step of the
counting method.
[0068] As discussed above, minAllowedPeriod is an estimate of the
minimum time needed to perform one repetition based on the fastest
repetition recorded of that exercise during the machine learning
process. In most cases it is much smaller than the actual time the
user spends per repetition. As such, there may be multiple
candidate peaks per actual exercise repetition. In the following
step, the actual exercise period around each candidate peak may be
estimated, and this estimation may be used to refine the set of
candidate peaks.
[0069] For example, for each candidate peak an autocorrelation may
be calculated in a window centered on the peak. The size of this
window may be set to be two times maxAllowedPeriod or a
predetermined duration (e.g., 9 seconds), whichever is smaller. The
largest autocorrelation value within the range of lags
[minAllowedPeriod, maxAllowedPeriod] may then be found. The lag at
which the maximum autocorrelation value occurs may be an estimate
of the exercise period for the candidate peak. Having computed
these estimates, the filtering process described above may be
repeated, with the exception that when considering whether to
accept a candidate peak, minAllowedPeriod may not be used as the
minimum allowed distance between the candidate peak and a
previously selected peak. Rather, the minimum allowed distance may
be set equal to 3/4 the estimated period for that candidate peak,
or another suitable ratio. This reduced set of candidate peaks may
form the input to the next step of the counting method.
[0070] Next, the set of candidate peaks may be filtered based on
peak amplitude. All of the candidate peaks may again be sorted
based on amplitude, and the 40th percentile largest peak denoted
(e.g. if there are 10 candidate peaks, the 4th-largest peak). All
peaks that have amplitude less than half the amplitude of this peak
may be disregarded. This method assumes that exercise repetitions
should in general have large-amplitude peaks as they involve motion
with high acceleration. Further, it may be assumed that within an
exercise set all of the repetitions should have about the same
amplitude. In other words, all peaks should be about as large as
one of the largest peaks.
[0071] The sign of the one-dimensional signal found by PCA is
arbitrary. However, this issue may be addressed in a number of
ways. For certain exercises there may be a particular accelerometer
axis which reliably corresponds to "up" and each repetition has one
peak in this "up" direction. As an example, this may be the case
for jumping jacks and the `x` axis in a particular set of sensors.
For these exercises, the PCA projection may be further manipulated
such that the sign of the "up" axis in the projection is positive,
allowing peaks to be counted in this example one-dimensional
signal. Controller 220 may designate the number of peaks as the
number of repetitions and the method may further comprise
outputting the number of repetitions.
[0072] The counting method may be run twice, once to count peaks
and once to count valleys. In one example, the method includes
determining a set of candidate valleys, filtering the set of
candidate valleys using local period estimates, filtering the set
of candidate valleys, counting a number of candidate valleys from
the set of candidate valleys, comparing the number of valleys to
the number of peaks, designating the greater of the number of
valleys and the number of peaks as a number of repetitions and
outputting the number of repetitions.
[0073] In an example where the counting method is performed during
the period where the user is actively engaged in a repetitive
physical activity or exercise, it may be possible for the output of
the peak counting method to decrease from one frame to the next, as
the PCA axes may change over time and the criteria for
amplitude-based rejection may shift over time as well. In order to
prevent confusing the user with a decreasing repetition count, the
method may not allow a count to decrease.
[0074] In some examples, the user may be performing a repetitive
physical activity or exercise in pursuit of a target number of
repetitions. In this example, the counting method may further
include the steps of determining that the user has stopped
performing a repetitive exercise based on the physical activity
attributes, comparing the number of repetitions determined by the
counting method to the target number of repetitions, and indicating
that the user has completed the repetitive exercise when the number
of repetitions determined by the counting method is within a
threshold (e.g., two) of the target number.
[0075] Further, the physical activity monitoring device may be
employed as part of a method of augmenting a gaming experience.
This may allow a user to take a gaming console experience and
extend it into the real world. A user may play a game inside the
home, and the game may further incorporate an out of home (or
otherwise untraditional) active gaming concept that employs a PAMD.
This may allow a user to play a game in the real world, with the
user's actions outside the immediate vicinity of the game console
ultimately being used as an aspect of gameplay. For example, an
avatar or game character controlled by the user may get stronger
based on the number of calories burned by the user while the user
runs for a determined time period or distance.
[0076] FIG. 7 shows one example method 700 of augmenting a game
experience with use of a PAMD. At 702, method 700 may include
receiving, at a PAMD from a game system, an indication of one or
more physical activities to be performed. The physical activities
may be performed as an extension of a game being played on the game
system. For example, the game may tell the user to run outside, and
the user may unlock gems at regular distance markers or calorie
burn targets along the way that in turn increase the user's level
in the gameplay inside the home. In some examples, the game may
tell the user to do a predetermined number of pushups, further
increasing the user's game score in the gameplay as the pushups are
performed.
[0077] At 704, method 700 may include measuring physical activity
attributes of a user wearing the physical activity monitoring
device. As described above, the PAMD may be configured to
automatically determine when a user is engaged in a physical
activity or exercise, and further configured to automatically
determine the type of physical activity or exercise being
undertaken by the user. This may further allow the game system to
assign a variety of physical activities or exercises to the
user.
[0078] At 706, method 700 may include determining the user's
progress towards completion of the one or more physical activities
based on the physical attributes. As described above, the PAMD may
be configured to count repetitions of a repetitive physical
activity or exercise, and further configured to monitor the
distance traveled by the user through signal information from a GPS
and/or accelerometer. This may allow the PAMD to give the user
tracking metrics and show the user's progress towards
completion.
[0079] At 708, method 700 may include outputting to the game device
an indication of the user's progress towards completion of the one
or more physical activities. The PAMD may communicate to the game
system as the user is performing the one or more physical
activities, for example with communication subsystem 250. This may
allow for in-game feedback to be delivered to the user from the
game device. The PAMD and/or the gaming system may be further
configured to sync data to a personal computer, mobile phone or
other devices, allowing for data integration and recording.
[0080] Method 700 may further include indicating to the user
information of the game as affected by progress towards completion
of the one or more physical activities. This may allow the user to
receive game feedback both while engaging with the game system and
also while using the PAMD outside the vicinity of the game system.
In one example, this may allow for the user to continue gameplay
while earning achievements that affect their game score or other
aspects of gameplay.
[0081] Method 700 may further include: at the physical activity
monitoring device, calculating one or more current biometric
markers for the user performing the one or more physical activities
and/or indicating to the user one or more current biometric
markers. The PAMD may be configured to give the user relevant
feedback for fitness tracking with information regarding heart rate
and personalized heart rate zones, GPS location information to view
routes covered, distance, steps taken, duration of exercise, number
of repetitions performed, time of day, an amount of calories burned
by the user performing the one or more physical activities, and
other relevant current biomarkers.
[0082] A PAMD in accordance with the present disclosure may be used
by a user to track a workout and record fitness metrics.
Furthermore, the PAMD may be configured to guide the user through a
workout and monitor the user's progress during the workout. In
other words, the physical activity monitoring device may serve as a
physical trainer.
[0083] FIG. 8 shows a method 800 of training a user of a PAMD. At
802, method 800 may include receiving at the PAMD a workout regimen
including a plurality of exercises. In one example, the user may
browse and select a workout on an app that runs on a personal
computer, mobile phone, or gaming console, and have that workout
regimen sent to the PAMD (e.g., via communication subsystem
250).
[0084] At 804, for each of the plurality of exercises included in a
workout regimen, method 800 may include indicating an exercise to a
user. The PAMD may include a display subsystem 280 and/or an audio
subsystem that is configured to signify information that walks the
user through the workout regimen step by step. For example, the
user may select a workout on the PAMD and request to begin the
workout. The PAMD may indicate to the user to do ten pushups, run
two miles, etc.
[0085] At 806, method 800 may include measuring physical activity
attributes of the user with the PAMD worn by the user as described
above. At 808, method 800 may include outputting information
regarding the user's progress towards completion of the exercise
based on the physical activity attributes. For example, while the
user performs pushups, the PAMD may recognize the activity and
count the number of repetitions completed or the number of
repetitions remaining for that exercise in the workout. The PAMD
may dynamically display the number of repetitions, as well as
display feedback regarding the user's heart rate, calories burned,
time spent working out and other metrics relative to the workout or
physical activity or exercise currently being performed by the
user.
[0086] If the user completes an exercise as indicated by the PAMD,
the method may further include: indicating to the user that an
exercise has been completed. For example, if the user completes an
exercise, an audible alarm or physical vibration may indicate that
the user has completed the physical activity or exercise and that
it is time to move on to the next physical activity or exercise in
the workout regimen.
[0087] At 810, method 800 may include determining if the workout
regimen has been completed. If the workout regimen includes
unfinished exercises, method 800 may return to 804, where method
800 may include indicating a next exercise to the user. In one
example, the information displayed by the display subsystem 280 may
change to indicate the next exercise to the user. In some examples,
the PAMD may give an audible cue to the user. If the workout
regimen has been completed, the PAMD may end, and may further
indicate the end of the workout to the user.
[0088] In some embodiments, the methods and processes described
above may be tied to a computing system of one or more computing
devices. In particular, such methods and processes may be
implemented as a computer-application program or service, an
application-programming interface (API), a library, and/or other
computer-program product.
[0089] Returning to FIG. 2, physical activity monitoring device 200
includes a logic machine 215 and a storage machine 217 which may
cooperate to instantiate controller 220, supervised classifier
230a, supervised classifier 230b, aggregator 240, communication
subsystem 250, reporter 260, voting machine 270 and/or display
subsystem.
[0090] Logic machine 215 includes one or more physical devices
configured to execute instructions. For example, the logic machine
may be configured to execute instructions that are part of one or
more applications, services, programs, routines, libraries,
objects, components, data structures, or other logical constructs.
Such instructions may be implemented to perform a task, implement a
data type, transform the state of one or more components, achieve a
technical effect, or otherwise arrive at a desired result.
[0091] Logic machine 215 may include one or more processors
configured to execute software instructions. Additionally or
alternatively, the logic machine may include one or more hardware
or firmware logic machines configured to execute hardware or
firmware instructions. Processors of the logic machine may be
single-core or multi-core, and the instructions executed thereon
may be configured for sequential, parallel, and/or distributed
processing. Individual components of the logic machine optionally
may be distributed among two or more separate devices, which may be
remotely located and/or configured for coordinated processing.
Aspects of the logic machine may be virtualized and executed by
remotely accessible, networked computing devices configured in a
cloud-computing configuration.
[0092] Storage machine 217 includes one or more physical devices
configured to hold instructions executable by the logic machine to
implement the methods and processes described herein. When such
methods and processes are implemented, the state of storage machine
217 may be transformed--e.g., to hold different data.
[0093] Storage machine 217 may include removable and/or built-in
devices. Storage machine 217 may include optical memory (e.g., CD,
DVD, HD-DVD, Blu-Ray Disc, etc.), semiconductor memory (e.g., RAM,
EPROM, EEPROM, etc.), and/or magnetic memory (e.g., hard-disk
drive, floppy-disk drive, tape drive, MRAM, etc.), among others.
Storage machine 217 may include volatile, nonvolatile, dynamic,
static, read/write, read-only, random-access, sequential-access,
location-addressable, file-addressable, and/or content-addressable
devices.
[0094] It will be appreciated that storage machine 217 includes one
or more physical devices. However, aspects of the instructions
described herein alternatively may be propagated by a communication
medium (e.g., an electromagnetic signal, an optical signal, etc.)
that is not held by a physical device for a finite duration.
[0095] Aspects of logic machine 215 and storage machine 217 may be
integrated together into one or more hardware-logic components.
Such hardware-logic components may include program- and
application-specific integrated circuits (PASIC/ASICs) or
system-on-a-chip (SOC), for example.
[0096] Display subsystem 280 may be used to present a visual
representation of data held by storage machine 217. This visual
representation may take the form of a graphical user interface
(GUI). As the herein described methods and processes change the
data held by the storage machine, and thus transform the state of
the storage machine, the state of display subsystem 280 may
likewise be transformed to visually represent changes in the
underlying data. Display subsystem 280 may include one or more
display devices utilizing virtually any type of technology. Such
display devices may be combined with logic machine 215 and/or
storage machine 217 in a shared enclosure, or such display devices
may be peripheral display devices.
[0097] Communication subsystem 250 may be configured to
communicatively couple PAMD 200 with one or more other computing
devices. Communication subsystem 250 may include wired and/or
wireless communication devices compatible with one or more
different communication protocols. As non-limiting examples, the
communication subsystem may be configured for communication via a
wireless telephone network, or a wired or wireless local- or
wide-area network. In some embodiments, the communication subsystem
may allow PAMD 200 to send and/or receive messages to and/or from
other devices via a network such as the Internet.
[0098] In examples where PAMD 200 contains a GPS and/or is
configured to collect biometric data concerning the user,
communication subsystem 250 may be configured to transmit data
about the user to one or more other computing devices. In some
examples, PAMD 200 and/or the one or more other computing devices
may be configured to inform the user regarding data that may be
collected and transmitted, and may be further configured to allow
the user to provide consent to allow the PAMD to collect, transmit
or otherwise share data regarding the user.
[0099] It will be understood that the configurations and/or
approaches described herein are exemplary in nature, and that these
specific embodiments or examples are not to be considered in a
limiting sense, because numerous variations are possible. The
specific routines or methods described herein may represent one or
more of any number of processing strategies. As such, various acts
illustrated and/or described may be performed in the sequence
illustrated and/or described, in other sequences, in parallel, or
omitted. Likewise, the order of the above-described processes may
be changed.
[0100] The subject matter of the present disclosure includes all
novel and nonobvious combinations and subcombinations of the
various processes, systems and configurations, and other features,
functions, acts, and/or properties disclosed herein, as well as any
and all equivalents thereof.
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