Latent load calibration for calorimetry using sensor fusion

• US 10,154,789 B2
• Filed: 09/30/2014
• Issued: 12/18/2018
• Est. Priority Date: 09/02/2014
• Status: Active Grant

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: Raghuram, Karthik Jayaraman, Pham, Hung A., Moore, III, Richard Channing, Singh Alvarado, Alexander, Srinivas, Umamahesh, Tan, Xing, Trietsch, Dan Marvin, Dervisoglu, Gunes, Mermel, Craig H., Huang, Ronald K., Ullal, Adeeti
• Primary Examiner(s): Downey, John R
• Application Number:
  US14/502,827
• Publication Number:
  US 20160058302A1
• Time in Patent Office: 1,540 Days
• 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

In one aspect, the present disclosure relates to a method including obtaining, by a heart rate sensor of a fitness tracking device, a heart rate measurement of a user of the fitness tracking device; obtaining, by at least one motion sensor, motion data of the user; analyzing, by the fitness tracking device, the motion data of the user to estimate a step rate of the user; estimating, by the fitness tracking device, a load associated with a physical activity of the user by comparing the heart rate measurement with the step rate of the user; and estimating, by the fitness tracking device, an energy expenditure rate of the user using the load and at least one of the heart rate measurement and the step rate.

Claims

1. A method comprising:

obtaining, by a heart rate sensor of a fitness tracking device, a heart rate measurement of a user of the fitness tracking device at a first sampling rate in response to determining that the user is in a particular activity session and at a second sampling rate at other times, wherein the first sampling rate is higher than the second sampling rate;

obtaining, by at least one motion sensor, motion data of the user;

analyzing, by the fitness tracking device, the motion data of the user to estimate a step rate of the user;

estimating, by the fitness tracking device, a load associated with a physical activity of the user by comparing the heart rate measurement with the step rate of the user; and

estimating, by the fitness tracking device, an energy expenditure rate of the user using a combination of the load and;

the heart rate measurement when the heart rate measurement is obtained at the first sampling rate, and

the step rate when the heart rate measurement is obtained at the second sampling rate.

2. The method of claim 1, wherein the physical activity of the user comprises training on an elliptical training machine, and wherein the load comprises a resistance setting of the elliptical training machine.

3. The method of claim 1, wherein the physical activity of the user comprises running on a treadmill, and wherein the load comprises an incline setting of the treadmill.

4. The method of claim 1, wherein the physical activity of the user comprises stationary cycling, and wherein the load comprises a resistance setting of a stationary cycle.

5. The method of claim 1, comprising:

analyzing, by the fitness tracking device, the motion data to determine a speed of the user,

wherein the physical activity of the user comprises running, and

wherein the load comprises a grade of a terrain on which the user comprises running.

6. The method of claim 1, wherein a first heart rate measurement indicates a first load for a given step rate, and wherein a second heart rate measurement different from the first heart rate measurement indicates a second load for the given step rate different from the first load for the given step rate.

7. The method of claim 1, wherein a first step rate indicates a first load for a given heart rate, and wherein a second step rate different from the first step rate indicates a second load for the given heart rate different from the first load for the given step rate.

8. A fitness tracking device comprising:

a heart rate sensor for measuring a heart rate of a user of the fitness tracking device at a first sampling rate in response to determining that the user is in a particular activity session and at a second sampling rate at other times, wherein the first sampling rate is higher than the second sampling rate;

a motion sensor for sensing motion of the user; and

a processor communicatively coupled to the heart rate sensor and the motion sensor, wherein the processor is configured to;

obtain a heart rate measurement of the user from the heart rate sensor;

obtain motion data of the user from the motion sensor;

analyze the motion data of the user to estimate a step rate of the user;

estimate a load associated with a physical activity of the user by comparing the heart rate measurement with the step rate of the user; and

estimate an energy expenditure rate of the user using a combination of the load and;

the heart rate measurement when the heart rate measurement is obtained at the first sampling rate, and

the step rate when the heart rate measurement is obtained at the second sampling rate.

9. The fitness tracking device of claim 8, wherein the physical activity of the user comprises training on an elliptical training machine, and wherein the load comprises a resistance setting of the elliptical training machine.

10. The fitness tracking device of claim 8, wherein the physical activity of the user comprises running on a treadmill, and wherein the load comprises an incline setting of the treadmill.

11. The fitness tracking device of claim 8, wherein the physical activity of the user comprises stationary cycling, and wherein the load comprises a resistance setting of a stationary cycle.

12. The fitness tracking device of claim 8, where the processor is configured to:

analyze the motion data to determine a speed of the user,

wherein the physical activity of the user comprises running, and

wherein the load comprises a grade of a terrain on which the user is running.

13. The fitness tracking device of claim 8, wherein a first heart rate measurement indicates a first load for a given step rate, and wherein a second heart rate measurement different from the first heart rate measurement indicates a second load for the given step rate different from the first load for the given step rate.

14. The fitness tracking device of claim 8, wherein a first step rate indicates a first load for a given heart rate, and wherein a second step rate different from the first step rate indicates a second load for the given heart rate different from the first load for the given step rate.

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 processor of a fitness tracking device comprising a heart rate sensor and a motion sensor and thereby cause the processor to operate to;

obtain a heart rate measurement of a user of the fitness tracking device from the heart rate sensor at a first sampling rate in response to determining that the user is in a particular activity session and at a second sampling rate at other times, wherein the first sampling rate is higher than the second sampling rate;

obtain motion data of the user of the fitness tracking device from the motion sensor;

analyze the motion data of the user to estimate a step rate of the user;

estimate a load associated with a physical activity of the user by comparing the heart rate measurement with the step rate of the user; and

estimate an energy expenditure rate of the user using a combination of the load and;

the heart rate measurement when the heart rate measurement is obtained at the first sampling rate, and

the step rate when the heart rate measurement is obtained at the second sampling rate.

16. The article of manufacture of claim 15, wherein the physical activity of the user comprises training on an elliptical training machine, and wherein the load comprises a resistance setting of the elliptical training machine.

17. The article of manufacture of claim 15, wherein the physical activity of the user comprises running on a treadmill, and wherein the load comprises an incline setting of the treadmill.

18. The article of manufacture of claim 15, wherein the physical activity of the user comprises stationary cycling, and wherein the load comprises a resistance setting of a stationary cycle.

19. The article of manufacture of claim 15, where the processor is caused to:

analyze the motion data to determine a speed of the user,

wherein the physical activity of the user comprises running, and

wherein the load comprises a grade of a terrain on which the user is running.

20. The article of manufacture of claim 15, wherein a first heart rate measurement indicates a first load for a given step rate, and wherein a second heart rate measurement different from the first heart rate measurement indicates a second load for the given step rate different from the first load for the given step rate.