Monitor device and use thereof

• US 20080275348A1
• Filed: 04/30/2008
• Published: 11/06/2008
• Est. Priority Date: 05/01/2007
• Status: Abandoned Application

• Current Assignee: Conopco (Unilever PLC)
• Original Assignee: Conopco (Unilever PLC)
• Inventors: Li, Ming, Catt, Michael, Weightman, Arthur Maurice
• Application Number:
  US12/150,679
• Publication Number:
  US 20080275348A1
• Time in Patent Office: Days
• US Class Current: 600/483
• CPC Class Codes: A61B 2503/10 Athletes
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• 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 5/1112 Global tracking of patients, e.g. by using GPS
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/1123 Discriminating type of movement, e.g. walking or running A61B5/1116, A61B5/112 take precedence
• CPC Class Codes: A61B 5/222 combined with detection or measurement of physiological parameters, e.g. heart rate
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A61B 5/726 using Wavelet transforms
• 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: G01C 22/006 Pedometers
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

In an apparatus and method for obtaining an indication of energy expenditure by a mammal during exercise, one or more movement transducers (1) each output a respective movement signal related to physical movement. A frequency analysis (3) is performed on at least one of the movement signals to obtain a frequency analysis result. Classification means (5) determines from the frequency analysis result, what class of physical movement is involved in the exercise. Selection means (7) selects a form of calculation according to a class determined by the classification means. A form of calculation selected by the selection means is applied (9) to at least one of the movement signals so as to obtain the energy expenditure indication.

Claims

1. An apparatus for obtaining an indication of energy expenditure by a mammal during exercise, the apparatus comprising:

(i) one or more moement transducers, each for outputting a respective movement signal related to physical movement;

(ii) analysis means for performing an analysis on at least one of the movement signals to obtain an analysis result;

(iii) classification means for determining from the analysis result, what class of physical movement is involved in the exercise;

(iv) selection means for selecting a form of calculation according to a class determined by the classification means; and

(v) means for applying a form of calculation selected by the selection means to at least one of the movement signals so as to obtain said energy expenditure indication.

2. An apparatus according to claim 1, wherein the analysis means is adapted to perform an analysis of the movement signals which comprises an analysis of frequency components.

3. An apparatus according to claim 2 wherein the analysis means is:

adapted to perform an analysis of frequency components which comprises Fourier analysis.

4. An apparatus according to claim 2 wherein the analysis means is:

adapted to perform an analysis of frequency components which comprises wavelet analysis.

5. An apparatus according to claim 1, wherein the analysis means is adapted to perform an analysis of the movement signals which comprises mapping of movement vectors to create a vector surface.

6. An apparatus according to claim 1, wherein the classification means is adapted to determine the class of physical movement from the analysis result by comparison of the analysis result with members of a library of stored data sets each indication of a respective different class of analysis result to determine which stored data set best matches the analysis result.

7. An apparatus according to claim 6, further comprising a memory for storing the stored data sets and calibration means for creating the stored data sets from calibration results obtained from the analysis means.

8. An apparatus according to claim 7 wherein the calibration means is adapted to update the stored data sets by means of an adaptive empirical method.

9. An apparatus according to claim 8, wherein the adaptive empirical method uses a Kalman filter or a neural network.

10. An apparatus according to claim 1, wherein said one or more transducers is or are, selected from any of accelerometers, velocity transducers and pedometers.

11. An apparatus according to claim 1, comprising at least two of said transducers arranged to produce a respective movement signal related to physical movement in respective different directions.

12. An apparatus according to claim 1, further comprising one or more secondary transducers, each for outputting a respective secondary indication signal, related to respective one or more physiological parameters.

13. An apparatus according to claim 12, wherein said classification means is arranged also to utilise said one or more secondary indication signals and/or to utilise respective signals derived from said one or more secondary indication signals, in order to determine said class of physical movement.

14. An apparatus according to claim 12, wherein said one or more secondary transducers are selected from heart rate transducers, peripheral pulse transducers and skin temperature transducers.

20. A method according to claim 1, wherein the class of physical movement is determined from the analysis result by comparison of the analysis result with members of a library of stored data sets each indicative of a respective different class of analysis result to determine which stored data set best matches the analysis result.

21. A method according to claim 20, wherein the stored data sets are created from calibration results obtained from the analysis means.

22. A method according to claim 21, wherein the stored data sets are updated by means of an adaptive empirical method.

23. A method according to claim 22, wherein the adaptive empirical method uses a Kalman filter or a neural network.

24. A method according to claim 1, wherein said one or more movement signals is or are, selected from any of signals related to acceleration, velocity and number of steps taken.

25. A method according to claim 1, wherein at least two movement signals are obtained, respectively related to physical movement in different directions.

26. A method according to claim 1, further comprising obtaining one or more secondary indication signals related to respective one or more physiological parameters.

27. A method according to claim 26, wherein said selection of a form of calculation according to the predetermined physical movement class also utilises said one or more secondary indication signals and/or utilises respective signals derived from said one or more secondary indication signals.

28. A method according to claim 1, wherein said one or more secondary indication signals are related to respective physiological parameters selected from heart rate, peripheral pulse and skin temperature.

15. A method of obtaining an indication of energy expenditure by a mammal during exercise, the method comprising:

(i) obtaining one or more movement signals related to physical movement;

(ii) performing an analysis on at least one of the movement signals to obtain an analysis result;

(iii) using the analysis result to determine what class of physical movement is involved in the exercise;

(iv) selecting a form of calculation according to the determined physical movement class; and

(v) applying the selected form of calculation to at least one of the movement signals to obtain said energy indication.

16. A method according to claim 15, wherein the analysis of the movement signals comprises an analysis of frequency components.

17. A method according to claim 16, wherein the analysis of frequency components comprises Fourier analysis.

18. A method according to claim 16, wherein the analysis of frequency components comprises wavelet analysis.

19. A method according to claim 15, wherein the analysis of the movement signals comprises mapping of movement vectors to create a vector surface.