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.

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.