Diary-free calorimeter

• US 8,182,424 B2
• Filed: 03/19/2008
• Issued: 05/22/2012
• Est. Priority Date: 03/19/2008
• Status: Active Grant

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Heckerman, David E.
• Primary Examiner(s): Johnson, III, Henry M
• Assistant Examiner(s): Rajan, Kai
• Application Number:
  US12/051,431
• Publication Number:
  US 20090240113A1
• Time in Patent Office: 1,525 Days
• Field of Search: 600300-301, 600/587, 128/905, 128920-921, 705 1- 4, 482/4, 482 8- 9
• US Class Current: 600/300
• CPC Class Codes: A61B 2503/10 Athletes
• 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 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/0531 Measuring skin impedance
• 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 5/1112 Global tracking of patients, e.g. by using GPS
• 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 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/7267 involving training the classification device
• 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 40/63 for local operation
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

Abstract

An indirect calorimeter estimates nutritional caloric intake by periodically monitoring weight and sensing physical exercise (i.e., physiological data and/or motion data related to physical exertion), which can then be used in a calorimetry model derived from regression analysis of a population (e.g., linear regression, feed-forward neural network, Gaussian process, boosted regression tree, etc.). A strap-on user device for tracking exercise can detect one or more of heart rate, body temperature, skin resistance, motion/acceleration sensing (e.g., pedometer, accelerometer), velocity sensing (e.g., global positioning system (GPS)), and an intelligent, integrated exercise machine (e.g., treadmill, exercise bike, etc.). To gain further fidelity, the user can fine-tune the estimate by undergoing a journal-based routine for a relatively short period of time or clinical calorimetry measurement (e.g., respiratory calorimeter), thereby providing a baseline for resting or exercising metabolic rate.

Claims

1. A processor-executable method for determining nutritional caloric intake, comprising:

detecting, from a portable sensor adapted to be worn by a user, a parameter associated with physical exercise over a measurement period;

comparing a current weight measurement of the user with a stored prior weight measurement at the beginning of the measurement period;

accessing a model for caloric intake stored in memory, the model for caloric intake being trained by regression analysis based upon caloric intake and weight changes monitored for a population of a plurality of individuals; and

estimating, by one or more processors, total caloric intake consumed by the user based upon the detected parameter, the prior weight, the current weight, and the caloric intake model.

2. The method of claim 1, further comprising:

periodically weighing the population; and

training the caloric intake model by regression analysis based upon the monitored caloric intake and population weights.

3. The method of claim 1, further comprising:

receiving a demographic classification for a user; and

accessing the model for caloric intake based upon the demographic classification.

4. The method of claim 1, further comprising detecting a parameter associated with body motion to determine physical exercise.

5. The method of claim 4, further comprising detecting a parameter associated with a selected one from a group consisting of global positioning satellites signals, pedometer signal, and an accelerometer signal.

6. The method of claim 1, further comprising detecting a parameter associated with physiological exertion to determine physical exercise.

7. The method of claim 6, further comprising detecting a parameter selected from a group consisting of elevated pulse, skin resistance, skin temperature, heart rate, and respiration rate.

8. The method of claim 1, further comprising detecting a parameter received from an exercise machine.

9. The method of claim 1, further comprising fine tuning a metabolic baseline for the user by receiving tracking data for caloric intake during an initial period for accessing the caloric intake model.

10. The method of claim 9, further comprising calculating caloric intake from a calorie journal.

11. The method of claim 1, further comprising receiving a weight measurement periodically from an electronically communicating scale.

13. The processor-executable method of claim 1, further comprising:

differentiating the population based on a plurality of demographic profiles; and

training the caloric intake model by regression analysis based upon the plurality of demographic profiles.

14. The processor-executable method of claim 13, wherein the plurality of demographic profiles include at least one of gender, age, height, and body mass index.

12. A processor-executable method for determining nutritional caloric intake, comprising:

detecting, from a first portable sensor adapted to be worn by a user, a physiological parameter associated with physical exercise over a measurement period;

detecting, from a second portable sensor adapted to be worn by the user, a motion parameter associated with physical exercise over the measurement period;

comparing a current weight measurement of the user with a stored prior weight measurement at the beginning of the measurement period;

accessing a model for caloric intake trained by performing regression analysis on caloric intake, weight changes, physiological sensing data, and exercise monitor data for a control population of a plurality of individuals, the model for caloric intake stored in memory; and

estimating, by one or more processors, total caloric intake consumed by the user based upon the detected physiological and motion parameters and the caloric intake model.

15. The processor-executable method of claim 12, wherein the model for caloric intake is trained by performing regression analysis on a plurality of demographic profiles for the control population.

16. The processor-executable method of claim 12, further comprising automatically generating one or more questions to ask to a doctor in response to the estimating of the total caloric intake.

17. A system comprising:

a processor; and

a memory communicatively coupled to the processor that stores computer-readable instructions that, when executed by the processor, perform operations comprising;

detecting a parameter associated with physical exercise over a measurement period, the parameters including physiological and motion parameters;

comparing a current weight measurement of the user with a stored prior weight measurement at the beginning of the measurement period;

accessing a model for caloric intake stored in memory, the model for caloric intake being trained by regression analysis based upon caloric intake and weight changes monitored for a control population of a plurality of individuals; and

estimating total caloric intake consumed by the user based upon the detected physiological and motion parameters, the prior weight, the current weight, and the caloric intake model.

18. The system of claim 17, wherein the operations further comprise:

receive additional weight measurements of the user to be used with the model for caloric intake to determine excess or deficit calories corresponding to a change in weight.

19. The system of claim 17, wherein the operations further comprise:

differentiate the population based on a plurality of demographic profiles; and

train the caloric intake model by regression analysis based upon the plurality of demographic profiles.

20. The system of claim 19, wherein the plurality of demographic profiles include at least one of gender, age, height, and body mass index.