Monitoring Energy Expended by an Individual

• US 20110288784A1
• Filed: 08/06/2011
• Published: 11/24/2011
• Est. Priority Date: 02/23/2009
• Status: Abandoned Application

• Current Assignee: Numera, Inc. (Innova Pure Water Incorporated)
• Original Assignee: Wellcore Corporation
• Inventors: Sapre, Rajendra Moreshwar, Jangle, Jeetendra
• Application Number:
  US13/204,658
• Publication Number:
  US 20110288784A1
• Time in Patent Office: Days
• US Class Current: 702/19
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• 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 5/221 Ergometry, e.g. by using bicycle type apparatus
• CPC Class Codes: A61B 5/4866 Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G06F 2218/00 Aspects of pattern recognition specially adapted for signal processing
• CPC Class Codes: G06V 40/23 Recognition of whole body movements, e.g. for sport training
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: G16H 20/60 relating to nutrition control, e.g. diets
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

Methods, apparatuses and systems of monitoring energy expended by an individual are disclosed. One method includes sensing, by a motion sensor, motion of the individual, identifying a plurality of activities performed by the individual over a period of time based on the identified motions, estimating, by a processor, energy expended by the individual for each of the plurality of the plurality of identified activities, and estimating energy expended by the individual by summing the estimated energy expended for each of the plurality of activities.

Claims

1. A method of monitoring energy expended by an individual, comprising:

sensing, by a motion sensor, motion of the individual;

identifying a plurality of activities performed by the individual over a period of time based on the identified motions;

estimating, by a processor, energy expended by the individual for each of the plurality of the plurality of identified activities; and

estimating energy expended by the individual by summing the estimated energy expended for each of the plurality of activities.

2. The method of claim 1, wherein identifying a plurality of activities comprises identifying a quasi-periodic activity.

3. The method of claim 2, wherein identifying a quasi-periodic activity comprises a training mode wherein training coefficients are generated, by sensing motion with the motion sensor while the individual performs a known activity.

4. The method of claim 3, wherein the training mode comprises generating a personal profile for the individual, or for group of individuals, wherein the profile comprises a plurality of parameters that are personalized to the individual or to the group.

5. The method of claim 4, wherein the quasi-periodic activity is identified based upon sensing motion of the individual, and the training coefficients.

6. The method of claim 4, wherein if the activity cannot be identified based on the training coefficients, tagging the unidentified activity as a new activity, and updating the training coefficients to include corresponding parameters that are personalized to the individual or the individual'"'"'s profile.

7. The method of claim 5, wherein identifying quasi-periodic activity further comprises:

generating an acceleration signature based on sensed acceleration of the individual,

matching the acceleration signature with at least one of a plurality of stored acceleration signatures, wherein each stored acceleration signatures corresponds with a type of motion;

identifying the type of motion of the object based on the matching of the acceleration signature with a stored acceleration signature.

8. The method of claim 7, wherein the type of motion comprises at least one of atomic motion, elemental motion and macro-motion.

9. The method of claim 7, wherein the stored acceleration signatures are stored in a common library and a specific library, and matching the acceleration signature comprises matching the acceleration signature with stored acceleration signatures of the common library, and then matching the acceleration signature with stored acceleration signatures of the specific library.

10. The method of claim 5, wherein identifying quasi-periodic activity further comprises:

generating an acceleration signature based on sensed acceleration of the individual,

identifying the type of motion of the individual based on discrimination of the acceleration signature using a training set, wherein the training set was generated during the training mode, and utilization of an artificial neural network.

11. The method of claim 1, wherein identifying a plurality of activities comprises identifying at least one non-quasi-periodic activity, comprising sensing an intensity and direction of the non-quasi-periodic activity.

12. The method of claim 1, wherein estimating energy expended by the individual for each of the plurality of identified activities comprise estimating the energy expended based on at least one of a quasi-periodicity of the activity, an intensity of the activity and a deviation of an acceleration magnitude from a base value.

13. The method of claim 1, wherein estimating the energy expended by the individual for each of the plurality of identified activities comprises estimating energy expended based on at least one of static orientation of the individual, a change of orientation of the individual, a time take to change the orientation of the individual, and a number of times the orientation changes within a given amount of time.

14. The method of claim 13, wherein estimating the energy expended by the individual for each of the plurality of activities comprises estimating energy expended based on statistical properties of an acceleration vector along a plurality of axes of orientation.

15. The method of claim 1, further comprising calculating a caloric burn of the individual based on the estimated energy expended.

16. The method of claim 15, further comprising estimating caloric intake of the individual.

17. The method of claim 16, further comprising comparing the caloric burn of the individual with the caloric intake of the individual, and estimating a weight change per unit time.

18. The method of claim 17, further comprising estimating a period required to meet a specific weight change target based on a weight change per unit time.

19. An apparatus for monitoring energy expended by an individual, comprising:

at least one motion sensing device sensing motion of the individual;

an artificial neural network receiving the sensed motion, accessing stored coefficients, and identifying at least one activity of the individual;

a controller operative to estimate energy expended by the individual for each of the plurality of identified activities, and estimate energy expended by the individual by summing the estimated energy expended for each of the plurality of activities.

20. The apparatus of claim 19, wherein identifying a plurality of activities comprises identifying a quasi-periodic activity.

21. The apparatus of claim 20, wherein identifying a quasi-periodic activity comprises a training mode wherein training coefficients are generated, by sensing motion with the motion sensor while the individual performs a known activity.

22. The apparatus of claim 21, wherein the training mode comprises generating a personal profile for the individual, or for group of individuals, wherein the profile comprises a plurality of parameters that are personalized to the individual or to the group.

23. The apparatus of claim 22, wherein the quasi-periodic activity is identified based upon sensing motion of the individual, and the training coefficients.