Method and apparatus for auto journaling of continuous or discrete body states utilizing physiological and/or contextual parameters

• US 8,157,731 B2
• Filed: 10/09/2003
• Issued: 04/17/2012
• Est. Priority Date: 10/09/2002
• Status: Expired due to Fees

• Current Assignee: JB IP Acquisitions LLC
• Original Assignee: BodyMedia Incorporated (AliphCom Corp.)
• Inventors: Teller, Eric, Farringdon, Jonathan, Andre, David, Pacione, Christopher, Stivoric, John, Safier, Scott, Pelletier, Raymond, Vishnubhatla, Suresh
• Primary Examiner(s): Johnson, III, Henry M
• Assistant Examiner(s): Rajan, Kai
• Application Number:
  US10/682,293
• Publication Number:
  US 20040133081A1
• Time in Patent Office: 3,113 Days
• Field of Search: 600/300, 600/301, 128903-905, 128/920
• US Class Current: 600/301
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 5/0002 Remote monitoring of patients using telemetry, e.g. transmission of vital signals via a communication network A61B5/07 takes precedence; transmission systems for measured values G08C; transmission H04B; transmission of digital information H04L; wireless communication networks H04W
• CPC Class Codes: A61B 5/002 Monitoring the patient using a local or closed circuit, e.g. in a room or building A61B5/0017 takes precedence
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• CPC Class Codes: A61B 5/01 Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/0533 Measuring galvanic skin response
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/0833 Measuring rate of oxygen consumption
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/369 Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/389 Electromyography [EMG]
• CPC Class Codes: A61B 5/398 Electrooculography [EOG], e.g. detecting nystagmus; Electroretinography [ERG]
• CPC Class Codes: A61B 5/411 Detecting or monitoring allergy or intolerance reactions to an allergenic agent or substance
• CPC Class Codes: A61B 5/4809 : Sleep detection, i.e. determining whether a subject is asleep or not
• CPC Class Codes: A61B 5/4866 : Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A61B 5/4872 : Body fat
• CPC Class Codes: A61B 5/4875 : Hydration status, fluid retention of the body
• CPC Class Codes: A61B 5/4884 : inducing physiological or psychological stress, e.g. applications for stress testing
• CPC Class Codes: A61B 5/6801 : specially adapted to be attached to or worn on the body surface
• CPC Class Codes: A61B 5/6824 : Arm or wrist
• CPC Class Codes: A61B 5/7225 : Details of analog processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation A61B5/30, A61B5/316 take precedence; transformers for specific applications H01F38/00; amplifiers H03F
• CPC Class Codes: A61B 5/7264 : Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 : involving training the classification device
• CPC Class Codes: A61B 5/7275 : Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7278 : Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/7282 : Event detection, e.g. detecting unique waveforms indicative of a medical condition A61B5/4094, A61B5/4818, A61B5/349, A61B5/0823 take precedence
• CPC Class Codes: A61B 5/743 : Displaying an image simultaneously with additional graphical information, e.g. symbols, charts, function plots
• CPC Class Codes: A61B 5/7445 : Display arrangements, e.g. multiple display units
• CPC Class Codes: A61B 5/7475 : User input or interface means, e.g. keyboard, pointing device, joystick
• CPC Class Codes: A63B 2024/0065 : Evaluating the fitness, e.g. fitness level or fitness index
• CPC Class Codes: A63B 24/0062 : Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
• CPC Class Codes: G06F 17/00 : Digital computing or data processing equipment or methods, specially adapted for specific functions information retrieval, database structures or file system structures therefor G06F16/00
• CPC Class Codes: G06F 8/00 : Arrangements for software engineering testing or debugging G06F11/36; administrative, planning or organisation aspects of software project management G06Q10/06
• CPC Class Codes: G16H 10/60 : for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 20/60 : relating to nutrition control, e.g. diets
• CPC Class Codes: G16H 40/63 : for local operation
• CPC Class Codes: G16H 40/67 : for remote operation
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: Y10S 128/905 : Feedback to patient of biological signal other than brain electric signal
• CPC Class Codes: Y10S 128/92 : Computer assisted medical diagnostics
• CPC Class Codes: Y10S 128/921 : Diet management

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.

Claims

1. An apparatus for deriving a state parameter of an individual, comprising:

a processor;

a sensor for generating a sensor output signal comprising data indicative of a rate of heat flowing off said individual'"'"'s body, and a sensor for generating a sensor output signal comprising data indicative of a resistance of said individual'"'"'s skin to an electric current, said sensor output signals being directed to an electronic communication link with said processor;

wherein said processor automatically determines a context of said individual with a naï

ve Bayesian classifier and a linear regression by using said data indicative of the rate of heat flowing off said individual'"'"'s body, and said data indicative of the resistance of said individual'"'"'s skin to an electric current; and

wherein said processor utilizes said context to predict the energy expenditure of said individual.

2. An apparatus according to claim 1, wherein said context comprises at least one of a resting and an active state of said individual.

3. An apparatus according to claim 1, said processor generating caloric consumption data for said individual, the apparatus further comprising a display which identifies said caloric expenditure data and said caloric consumption data.

4. An apparatus according to claim 3, further comprising an input device in electronic communication with said processor, said caloric consumption data being determined from information collected by said input device from said individual relating to foods eaten by said individual.

5. An apparatus according to claim 3, wherein said displayed information includes energy balance data.

6. An apparatus according to claim 3, wherein said displayed information includes a rate of weight loss or gain of said individual.

7. An apparatus according to claim 3, wherein said displayed information includes information relating to one or more goals of said individual, said goals relating to the monitoring and status of one or more of caloric consumption, caloric expenditure, energy balance and rate of weight loss or gain for said individual.

8. An apparatus according to claim 1, said apparatus further comprising a wearable sensor device including said sensors which is mounted on said individual, said processor being included in a computing device located separately from said sensor device, each of said computing device and said sensor device having transmitting and receiving circuitry for generating and receiving electronic signals which include said electronic communication link.

9. A method of measuring a state parameter of an individual, comprising:

collecting data indicative of a resistance of said individual'"'"'s skin to an electric current from a GSR sensor;

collecting data indicative of a rate of heat flowing off said individual'"'"'s body;

utilizing a processor to automatically determine a context of said individual based on the data indicative of the resistance of said individual'"'"'s skin to an electric current, and the data indicative of the rate of heat flowing off said individual'"'"'s body; and

utilizing said context to predict an energy expenditure of said individual.

10. A method according to claim 9, wherein said context comprises at least one of rest and active.

11. A method according to claim 9, wherein the processor uses a naï

ve Bayesian classifier to determine the context.

12. A method according to claim 9, further comprising generating caloric consumption data for said individual and displaying information based on said caloric expenditure data and said caloric consumption data.

13. A method according to claim 12, said caloric consumption data being generated from information collected from said individual relating to foods eaten by said individual.

14. A method according to claim 12, wherein said displayed information includes energy balance data.

15. A method according to claim 12, wherein said displayed information includes a rate of weight loss or gain of said individual.

16. A method according to claim 12, wherein said displayed information includes information relating to one or more goals of said individual, said goals relating to one or more of caloric consumption, caloric expenditure, energy balance and rate of weight loss or gain.

17. An apparatus for deriving a state parameter of an individual, comprising:

a processor;

a sensor for generating a sensor output signal comprising data indicative of a rate of heat flowing off said individual'"'"'s body;

a sensor for generating a sensor output signal comprising data indicative of a resistance of said individual'"'"'s skin to an electric current, said sensor output signals being directed to an electronic communication link with said processor;

wherein said processor automatically determines a context of said individual by using said data indicative of the rate of heat flowing off said individual'"'"'s body, and said data indicative of the resistance of said individual'"'"'s skin to an electric current; and

wherein said processor utilizes said context to predict the energy expenditure of said individual.

18. The apparatus according to claim 17, wherein the processor uses a naï

ve Bayesian classifier and a linear regression to determine the context.

19. The apparatus according to claim 17, wherein the processor uses a regression algorithm to determine each context.

20. The apparatus according to claim 17, wherein the processor computes a longitudinal accelerometer average.

21. The apparatus according to claim 17, wherein the processor computes a transverse accelerometer sum of average differences.

22. The apparatus according to claim 17, wherein the processor computes a heat flux.

23. The apparatus according to claim 17, wherein the processor computes a vector sum of transverse and longitudinal accelerometer sum of absolute differences.

24. The apparatus according to claim 17, wherein the processor computes a basal metabolic rate.

25. The apparatus according to claim 17, wherein the processor further comprises a context detector, the context detector comprising a naï

ve Bayesian classifier for predicting a context of the individual using at least one of a longitudinal accelerometer average, a transverse accelerometer sum of average differences and a heat flux.