Systems and methods for measuring energy expenditure of an individual

Abstract

Various methods and apparatuses for measuring a state parameter of an individual using signals based on one or more sensors are disclosed. In one embodiment, a first set of signals is used in a first function to determine how a second set of signals is used in one or more second functions to predict the state parameter. In another embodiment, first and second functions are used where the state parameter or an indicator of the state parameter may be obtained from a relationship between the first function and the second function. The state parameter may, for example, include calories consumed or calories burned by the individual. Various methods for making such apparatuses are also disclosed.

1 . (canceled) 
     
     
         2 . A method of measuring energy expenditure of an individual, comprising:
 collecting a plurality of sensor signals from at least two of a body motion sensor, a heat flux sensor, a skin conductance sensor, and a skin temperature sensor, each in electronic communication with a sensor device worn on a body of said individual; and   utilizing a first set of signals based on one or more of said plurality of sensor signals in one or more functions to predict said energy expenditure of said individual.   
     
     
         3 . A method according to  claim 2 , said collecting step comprising collecting said plurality of sensor signals from a body motion sensor, a heat flux sensor, and a skin conductance sensor. 
     
     
         4 . A method according to  claim 3 , said body motion sensor being an accelerometer and said skin conductance sensor being a GSR sensor. 
     
     
         5 . A method according to  claim 2 , said utilizing step comprising utilizing said first set of signals in a first function, said first function determining how a second set of signals based on one or more of said plurality of sensor signals is utilized in one or more second functions, each of said one or more second functions having an output; wherein one or more of said outputs are used to predict said energy expenditure of said individual. 
     
     
         6 . A method according to  claim 5 , wherein said first function recognizes one or more contexts based on said first set of signals, wherein one or more of said second functions is chosen based on said one or more recognized contexts, and wherein said outputs of said chosen second functions are used to predict said energy expenditure of said individual. 
     
     
         7 . A method according to  claim 5 , wherein said first function recognizes each of a plurality of contexts based on said first set of signals, wherein each of said one or more second functions corresponds to one of said contexts, wherein said first function assigns a weight to each of said one or more second functions based on a recognition probability associated with the corresponding context, and wherein said outputs of said one or more second functions and said weights are used to predict said energy expenditure of said individual. 
     
     
         8 . A method according to  claim 5 , further comprising combining one or more of said outputs in a post processing step to predict said energy expenditure of said individual. 
     
     
         9 . A method according to  claim 5 , wherein said second functions are regression algorithms. 
     
     
         10 . A method according to  claim 5 , wherein said contexts comprise rest and active. 
     
     
         11 . A method according to  claim 5 , wherein said first function comprising a naïve Bayesian classifier. 
     
     
         12 . A method according to  claim 5 , said collecting step comprising collecting said plurality of sensor signals from a body motion sensor, a heat flux sensor, and a skin conductance sensor, said second set of signals comprising a heat flux high gain average variance (HFvar), a vector sum of transverse and longitudinal accelerometer SADs (VSAD), and a galvanic skin response low gain (GSR), wherein said second functions have the form of A*VSAD+B*HF+C*GSR+D*BMR+E, wherein A, B, C, D and E are constants and BMR is a basal metabolic rate for said individual. 
     
     
         13 . An apparatus for measuring energy expenditure of an individual, comprising a processor;
 at least two of a body motion sensor, a heat flux sensor, a skin conductance sensor, and a skin temperature sensor in electronic communication with said processor; and   a memory storing software executable by said processor, said software including instructions for:
 collecting a plurality of sensor signals from said at least two of a body motion sensor, a heat flux sensor, a skin conductance sensor, and a skin temperature sensor; and 
 utilizing a first set of signals based on one or more of said plurality of sensor signals in one or more functions to predict said energy expenditure of said individual. 
   
     
     
         14 . An apparatus according to  claim 13 , said collecting instruction comprising collecting said plurality of sensor signals from a body motion sensor, a heat flux sensor, and a skin conductance sensor. 
     
     
         15 . An apparatus according to  claim 14 , said body motion sensor being an accelerometer and said skin conductance sensor being a GSR sensor. 
     
     
         16 . An apparatus according to  claim 13 , said utilizing instruction comprising utilizing said first set of signals in a first function, said first function determining how a second set of signals based on one or more of said plurality of sensor signals is utilized in one or more second functions, each of said one or more second functions having an output; wherein one or more of said outputs are used to predict said energy expenditure of said individual. 
     
     
         17 . An apparatus according to  claim 16 , wherein said first function recognizes one or more contexts based on said first set of signals, wherein one or more of said second functions is chosen based on said one or more recognized contexts, and wherein said outputs of said chosen second functions are used to predict said energy expenditure of said individual. 
     
     
         18 . An apparatus according to  claim 16 , wherein said first function recognizes each of a plurality of contexts based on said first set of signals, wherein each of said one or more second functions corresponds to one of said contexts, wherein said first function assigns a weight to each of said one or more second functions based on a recognition probability associated with the corresponding context, and wherein said outputs of said one or more second functions and said weights are used to predict said energy expenditure of said individual. 
     
     
         19 . An apparatus according to  claim 16 , said instructions further comprising combining said one or more outputs in a post processing step to predict said energy expenditure of said individual. 
     
     
         20 . An apparatus according to  claim 16 , wherein said second functions are regression algorithms. 
     
     
         21 . An apparatus according to  claim 20 , wherein said contexts comprise rest and active. 
     
     
         22 . An apparatus according to  claim 21 , said first function comprising a naive Bayesian classifier. 
     
     
         23 . An apparatus according to  claim 21 , wherein said collecting instruction comprising collecting said plurality of sensor signals from a body motion sensor, a heat flux sensor, and a skin conductance sensor, said second set of signals comprising a heat flux high gain average 15 variance (HFvar), a vector sum of transverse and longitudinal accelerometer SADs (VSAD), and a galvanic skin response low gain (GSR), wherein said second functions have the form of A*VSAD+B*HF+C*GSR+D*BMR+E, wherein A, B, C, D and E are constants and BMR is a basal metabolic rate for said individual.