Local model for calorimetry

• US 10,098,549 B2
• Filed: 09/30/2014
• Issued: 10/16/2018
• Est. Priority Date: 09/02/2014
• Status: Active Grant

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: Tan, Xing, Pham, Hung A., Moore, III, Richard Channing, Raghuram, Karthik Jayaraman, Singh Alvarado, Alexander, Srinivas, Umamahesh, Agarwal, Mrinal, Arnold, Edith Merle
• Primary Examiner(s): Brusca, John S
• Assistant Examiner(s): Wise, Olivia M
• Application Number:
  US14/501,701
• Publication Number:
  US 20160058332A1
• Time in Patent Office: 1,477 Days
• Field of Search: None
• CPC Class Codes: A61B 2560/0204 of power management
• CPC Class Codes: A61B 2560/0242 adapted to measure environmental factors, e.g. temperature, pollution
• CPC Class Codes: A61B 2560/0475 Special features of memory means, e.g. removable memory cards
• 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 5/0004 characterised by the type of physiological signal transmitted
• 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 5/02416 using photoplethysmograph signals, e.g. generated by infrared radiation A61B5/14552 takes precedence
• CPC Class Codes: A61B 5/02438 with portable devices, e.g. worn by the patient
• CPC Class Codes: A61B 5/1112 Global tracking of patients, e.g. by using GPS
• CPC Class Codes: A61B 5/1116 Determining posture transitions
• CPC Class Codes: A61B 5/1118 Determining activity level
• CPC Class Codes: A61B 5/112 Gait analysis
• CPC Class Codes: A61B 5/1121 Determining geometric values, e.g. centre of rotation or angular range of movement
• 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 5/4884 inducing physiological or psychological stress, e.g. applications for stress testing
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• CPC Class Codes: A61B 5/7221 : Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: A61B 5/7246 : using correlation, e.g. template matching or determination of similarity
• CPC Class Codes: A61B 5/725 : using specific filters therefor, e.g. Kalman or adaptive filters A61B5/316 take precedence
• 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 5/7275 : 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: A63B 22/0605 : performing a circular movement, e.g. ergometers
• CPC Class Codes: G01C 22/006 : Pedometers

Abstract

A fitness tracking device configured to be worn by a user obtains a plurality of physical characteristics of the user including a first age and a sex of the user. The fitness tracking device maps each physical characteristic of the user to a corresponding index, wherein the first age of the user is mapped to a first age index of a first age range of a plurality of age ranges, and wherein the sex of the user is mapped to a first sex index. The fitness tracking device selects, from a memory of the fitness tracking device, a first calorimetry model of a plurality of calorimetry models, wherein the first calorimetry model is associated with each corresponding index, including the first age index and the first sex index of the user. The fitness tracking device estimates an energy expenditure rate using the first calorimetry model.

Claims

1. A method of limiting battery power usage, memory usage, processing cycle usage, or a combination thereof in a fitness tracking device configured to be worn by a user and configured to determine energy expenditure of a user, the method comprising:

obtaining, by the fitness tracking device configured to be worn by a user, a plurality of physical characteristics of the user, wherein the plurality of physical characteristics comprise a first age and a sex of the user, the fitness tracking device including a memory configured to store an age lookup table comprising a plurality of age indices of a plurality of age ranges and a sex lookup table comprising plurality of sex indices of a plurality of sexes;

mapping, by the fitness tracking device, each physical characteristic of the user to a corresponding index, wherein the first age of the user is mapped to a first age index of a first age range of the plurality of age ranges, and wherein the sex of the user is mapped to a first sex index of the plurality of sexes;

selecting, from the memory of the fitness tracking device, a first combination of coefficients of a plurality of coefficients, wherein the first combination of coefficients is associated with each corresponding index, including the first age index and the first sex index of the user, wherein each of the plurality of coefficients are stored in the memory of the fitness tracking device, and wherein the combination of coefficients defines a first calorimetry model for the user;

collecting, by a motion sensor of the fitness tracking device, sensed motion data of the user, wherein the fitness tracking device further comprises a low-power motion coprocessor, the low-power motion coprocessor communicatively coupled to the motion sensor;

receiving, by the low-power motion coprocessor, the sensed motion data;

analyzing, by the low-power motion coprocessor, the sensed motion data to determine a speed of the user;

estimating an energy expenditure rate using the first calorimetry model and the speed of the user; and

outputting an indication of energy expenditure for the user.

2. The method of claim 1, comprising:

obtaining, by the fitness tracking device, a plurality of updated physical characteristics of the user, wherein the plurality of updated physical characteristics comprises an updated age different from the first age of the user; and

mapping, by the fitness tracking device, each updated physical characteristic of the user to the corresponding index, wherein the updated age is mapped to the first age index of the first age range comprising the first age and the updated age,

wherein the first calorimetry model used to estimate the energy expenditure rate approximates the energy expenditure rate for each age of the first age range, including the first age and updated age.

3. The method of claim 1, wherein the plurality of physical characteristics comprises a first body mass index (BMI) of the user, wherein the first BMI is mapped to a first BMI index of a first BMI range, and wherein the first combination of coefficients is associated with the combination of the first BMI index, the first age index, and the first sex index.

4. The method of claim 3, where obtaining the BMI of the user comprises:

obtaining, by the fitness tracking device, a weight of the user; and

using the weight of the user to estimate, by the fitness tracking device, the BMI of the user.

5. The method of claim 1, wherein the total number of calorimetry models defined by the plurality of coefficients stored in the memory of the fitness tracking device comprises a product of a total number of the age indices and a total number of the sex indices.

6. The method of claim 1, wherein the motion sensor comprises an accelerometer.

7. The method of claim 1, wherein the motion sensor comprises a gyroscope.

8. A fitness tracking device configured to be worn by a user and configured to limit battery power usage, memory usage, processing cycle usage, or a combination thereof while determining energy expenditure of the user, the device comprising:

a battery;

a memory configured to store an age lookup table comprising a plurality of age indices of a plurality of age ranges and a sex lookup table comprising a plurality of sex indices of a plurality of sexes;

a motion sensor configured to obtain motion data of the user; and

a low-power motion coprocessor communicatively coupled to the memory and the motion sensor, and wherein the low-power motion coprocessor is configured to;

obtain a plurality of physical characteristics of the user, wherein the plurality of physical characteristics comprise a first age and a sex of the user;

map each physical characteristic of the user to a corresponding index, wherein the first age of the user is mapped to a first age index of a first age range of the plurality of age ranges, and wherein the sex of the user is mapped to a first sex index of the plurality of sexes;

select, from the memory, a first combination of coefficients of a plurality of coefficients, wherein the first combination of coefficients is associated with each corresponding index, including the first age index and the first sex index of the user, wherein each of the plurality of coefficients are stored in the memory of the fitness tracking device, and wherein the combination of coefficients defines a first calorimetry model for the user;

receive, from the motion sensor, sensed motion data of the user; and

analyze the sensed motion data to determine a speed of the user;

and wherein the fitness tracking device is configured to;

estimate an energy expenditure rate using the first calorimetry model and the speed of the user; and

output an indication of energy expenditure for the user.

9. The fitness tracking device of claim 8, wherein the processor is configured to:

obtain a plurality of updated physical characteristics of the user, wherein the plurality of updated physical characteristics comprises an updated age different from the first age of the user; and

map each updated physical characteristic of the user to the corresponding index, wherein the updated age is mapped to the first age index of the first age range comprising the first age and the updated age,

wherein the first calorimetry model used to estimate the energy expenditure rate approximates the energy expenditure rate for each age of the first age range, including the first age and updated age.

10. The fitness tracking device of claim 8, wherein the plurality of physical characteristics comprises a first body mass index (BMI) of the user, wherein the first BMI is mapped to a first BMI index of a first BMI range, and wherein the first combination of coefficients is associated with the combination of the first BMI index, the first age index, and the first sex index.

11. The fitness tracking device of claim 10, wherein the processor is configured to:

obtain a weight of the user; and

use the weight of the user to estimate the BMI of the user.

12. The fitness tracking device of claim 8, wherein the total number of calorimetry models defined by the plurality of coefficients stored in the memory of the fitness tracking device comprises a product of a total number of the age indices and a total number of the sex indices.

13. The fitness tracking device of claim 8, wherein the motion sensor comprises an accelerometer.

14. The fitness tracking device of claim 8, wherein the motion sensor comprises a gyroscope.

15. An article of manufacture comprising:

a non-transitory processor readable storage medium; and

instructions stored on the medium;

wherein the instructions are configured to be readable from the medium by a low-power motion coprocessor of a fitness tracking device, wherein the fitness tracking device comprises a memory configured to store an age lookup table comprising a plurality of age indices of a plurality of age ranges and a sex lookup table comprising a plurality of sex indices of a plurality of sexes and a motion sensor and wherein the instructions thereby cause the low-power motion coprocessor to operate to;

obtain a plurality of physical characteristics of the user, wherein the plurality of physical characteristics comprise a first age and a sex of the user;

map each physical characteristic of the user to a corresponding index, wherein the first age of the user is mapped to a first age index of a first age range of the plurality of age ranges, and wherein the sex of the user is mapped to a first sex index of the plurality of sexes;

select, from the memory, a first combination of coefficients of a plurality of coefficients, wherein the first combination of coefficients is associated with each corresponding index, including the first age index and the first sex index of the user, wherein each of the plurality of coefficients are stored in the memory of the fitness tracking device, and wherein the combination of coefficients defines a first calorimetry model for the user;

receive, from the motion sensor, sensed motion data of the user; and

analyze the sensed motion data to determine a speed of the user;

and wherein the fitness tracking device is configured to;

estimate an energy expenditure rate using the first calorimetry model and the speed of the user; and

output an indication of energy expenditure for the user.

16. The article of manufacture of claim 15, wherein the processor is caused to:

obtain a plurality of updated physical characteristics of the user, wherein the plurality of updated physical characteristics comprises an updated age different from the first age of the user; and

map each updated physical characteristic of the user to the corresponding index, wherein the updated age is mapped to the first age index of the first age range comprising the first age and the updated age,

wherein the first calorimetry model used to estimate the energy expenditure rate approximates the energy expenditure rate for each age of the first age range, including the first age and updated age.

17. The article of manufacture of claim 15, wherein the plurality of physical characteristics comprises a first body mass index (BMI) of the user, wherein the first BMI is mapped to a first BMI index of a first BMI range, and wherein the first combination of coefficients is associated with the combination of the first BMI index, the first age index, and the first sex index.

18. The article of manufacture of claim 15, wherein the total number of calorimetry models defined by the plurality of coefficients stored in the memory of the fitness tracking device comprises a product of a total number of the age indices and a total number of the sex indices.

19. The article of manufacture of claim 15, wherein the motion sensor comprises an accelerometer.

20. The article of manufacture of claim 15, wherein the motion sensor comprises a gyroscope.