Systems and methods of measuring caloric consumption

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 caloric consumption of an individual for a time period, comprising:
 determining a weight differential for said individual between a beginning of said time period and an end of said time period;   multiplying said weight differential by a constant to obtain a caloric differential;   measuring a caloric expenditure of said individual for said time period using a wearable sensor device having one or more sensors; and   determining said caloric consumption from said caloric differential and said caloric expenditure.   
     
     
         3 . A method according to  claim 2 , wherein said constant is 3500. 
     
     
         4 . A method according to  claim 2 , wherein said step of measuring said caloric expenditure comprises:
 collecting a plurality of sensor signals from at least two sensors in electronic communication with said sensor device, at least one of said sensors being a physiological sensor; and   utilizing a first set of signals based on one or more of said plurality of sensor 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 caloric expenditure.   
     
     
         5 . A method according to  claim 4 , 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 caloric expenditure. 
     
     
         6 . A method according to  claim 4 , 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 caloric expenditure. 
     
     
         7 . A method according to  claim 4 , said utilizing step further comprising combining said one or more outputs in a post processing step to predict said caloric expenditure. 
     
     
         8 . A method according to claim  162 , wherein said second functions are regression algorithms. 
     
     
         9 . A method according to  claim 2 , wherein said contexts comprise rest and active. 
     
     
         10 . A method according to  claim 9 , said first function comprising a naive Bayesian classifier. 
     
     
         11 . A method according to  claim 4 , said at least two sensors comprising a body motion sensor, a heat flux sensor and a skin conductance sensor. 
     
     
         12 . A method according to  claim 11 , said body motion sensor being an accelerometer and said skin conductance sensor being a GSR sensor.