ACCURATE CALORIMETRY FOR INTERMITTENT EXERCISES

• US 20160058370A1
• Filed: 09/30/2014
• Published: 03/03/2016
• 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, Richard Channing III, SINGH ALVARADO, Alexander, SRINIVAS, Umamahesh, TAN, Xing
• Granted Patent: US 10,524,670 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• 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 fitness tracking device, a plurality of heart rate measurements of the user over a period of time, wherein the plurality of heart rate measurements can include heart rate data from a heart rate sensor of the fitness tracking device; analyzing, by the fitness tracking device, the plurality of heart rate measurements to determine a rate of change of a heart rate of the user during the period of time; determining, by the fitness tracking device, that the user is experiencing an onset phase if the rate of change of the heart rate during the period of time is greater than zero; determining, by the fitness tracking device, that the user is experiencing a cool-down phase if the rate of change of the heart rate during the period of time is less than zero; estimating, by the fitness tracking device, a first rate of energy expenditure of the user if the user is experiencing an onset phase using an onset calorimetry model; and estimating, by the fitness tracking device, a second rate of energy expenditure of the user if the user is experiencing a cool-down phase using a cool-down calorimetry model.

Claims

1. A method comprising:

obtaining, by a fitness tracking device, a plurality of heart rate measurements of the user over a period of time, wherein the plurality of heart rate measurements comprises heart rate data from a heart rate sensor of the fitness tracking device;

analyzing, by the fitness tracking device, the plurality of heart rate measurements to determine a rate of change of a heart rate of the user during the period of time;

determining, by the fitness tracking device, that the user is experiencing an onset phase if the rate of change of the heart rate during the period of time is greater than zero;

determining, by the fitness tracking device, that the user is experiencing a cool-down phase if the rate of change of the heart rate during the period of time is less than zero;

estimating, by the fitness tracking device, a first rate of energy expenditure of the user if the user is experiencing an onset phase using an onset calorimetry model; and

estimating, by the fitness tracking device, a second rate of energy expenditure of the user if the user is experiencing a cool-down phase using a cool-down calorimetry model.

2. The method of claim 1, comprising:

estimating, by the fitness tracking device, that the user is experiencing the onset phase if the rate of change of the heart rate is increasing over the period of time; and

estimating, by the fitness tracking device, that the user is experiencing the cool-down phase if the rate of change of the heart rate is decreasing over the period of time.

3. The method of claim 1, comprising:

obtaining, by the fitness tracking device, motion data of the user over the period of time, wherein the motion data comprises a first plurality motion measurements from a first motion sensor of the fitness tracking device;

determining, by the fitness tracking device, using the motion data of the user, an activity context of the user, wherein the activity context indicates that the user is engaged in an intermittent exercise activity comprising intermittent periods of rest or reduced exercise intensity; and

adjusting, by the fitness tracking device, the onset calorimetry model and the cool-down calorimetry model according to a type of the intermittent exercise activity.

4. The method of claim 1, wherein the type of the intermittent exercise activity comprises at least one of cross-training, interval running, yoga, and weight lifting.

5. The method of claim 1, comprising:

analyzing, by the fitness tracking device, the plurality of heart rate measurements to determine a first intensity level of the user during the period of time;

estimating, by the fitness tracking device, that the user is experiencing the onset phase if the first intensity level of the user is greater than a first threshold intensity level; and

estimating, by the fitness tracking device, that the user is experiencing the cool-down phase if the first intensity level of the user is less than the first threshold intensity level.

6. The method of claim 1, wherein the cool-down calorimetry model comprises a fast-decay calorimetry model and a slow-decay calorimetry model.

7. The method of claim 1, comprising:

determining, by the fitness tracking device, that the user switched from higher-intensity exercise during a prior period of time to a lower-intensity exercise or to a state of rest during the period of time; and

switching, by the fitness tracking device, from using the onset calorimetry model to using the cool-down calorimetry model.

8. A fitness tracking device comprising:

a heart rate sensor for obtaining heart rate measurements of a user of the fitness tracking device;

a motion sensor for obtaining motion data 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 plurality of heart rate measurements of the user over a period of time, wherein the plurality of heart rate measurements comprise heart rate data from the heart rate sensor;

analyze the plurality of heart rate measurements to determine a rate of change of a heart rate of the user during the period of time;

determine that the user is experiencing an onset phase if the rate of change of heart rate during the period of time is greater than zero;

determine that the user is experiencing a cool-down phase if the rate of change of the heart rate during the period of time is less than zero;

estimate a first rate of energy expenditure of the user if the user is experiencing an onset phase using an onset calorimetry model; and

estimate a second rate of energy expenditure of the user if the user is experiencing a cool-down phase using a cool-down calorimetry model.

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

estimate that the user is experiencing the onset phase if the rate of change of the heart rate is increasing over the period of time; and

estimate that the user is experiencing the cool-down phase if the rate of change of the heart rate is decreasing over the period of time.

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

obtain motion data of the user over the period of time, wherein the motion data comprises a first plurality motion measurements from a first motion sensor of the fitness tracking device;

determine, using the motion data of the user, an activity context of the user, wherein the activity context indicates that the user is engaged in an intermittent exercise activity comprising intermittent periods of rest or reduced exercise intensity; and

adjust the onset calorimetry model and the cool-down calorimetry model according to a type of the intermittent exercise activity.

11. The fitness tracking device of claim 8, wherein the type of the intermittent exercise activity comprises at least one of cross-training, interval running, yoga, and weight lifting.

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

analyze the plurality of heart rate measurements to determine a first intensity level of the user during the period of time;

estimate that the user is experiencing the onset phase if the first intensity level of the user is greater than a first threshold intensity level; and

estimate that the user is experiencing the cool-down phase if the first intensity level of the user is less than the first threshold intensity level.

13. The fitness tracking device of claim 8, wherein the cool-down calorimetry model comprises a fast-decay calorimetry model and a slow-decay calorimetry model.

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

determine that the user switched from higher-intensity exercise during a prior period of time to a lower-intensity exercise or to a state of rest during the period of time; and

switch from using the onset calorimetry model to using the cool-down calorimetry model.

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, wherein the fitness tracking device comprises a heart rate sensor for obtaining heart rate measurements of a user of the fitness tracking device and a motion sensor for obtaining motion data of the user and the instructions thereby cause the processor to operate to;

obtain a plurality of heart rate measurements of the user over a period of time, wherein the plurality of the heart rate measurements comprise heart rate data from the heart rate sensor;

analyze the plurality of heart rate measurements to determine a rate of change of a heart rate of the user during the period of time;

determine that the user is experiencing an onset phase if the rate of change of heart rate during the period of time is greater than zero;

determine that the user is experiencing a cool-down phase if the rate of change of the heart rate during the period of time is less than zero;

estimate a first rate of energy expenditure of the user if the user is experiencing an onset phase using an onset calorimetry model; and

estimate a second rate of energy expenditure of the user if the user is experiencing a cool-down phase using a cool-down calorimetry model.

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

estimate that the user is experiencing the onset phase if the rate of change of the heart rate is increasing over the period of time; and

estimate that the user is experiencing the cool-down phase if the rate of change of the heart rate is decreasing over the period of time.

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

obtain motion data of the user over the period of time, wherein the motion data comprises a first plurality motion measurements from a first motion sensor of the fitness tracking device;

determine, using the motion data of the user, an activity context of the user, wherein the activity context indicates that the user is engaged in an intermittent exercise activity comprising intermittent periods of rest or reduced exercise intensity; and

adjust the onset calorimetry model and the cool-down calorimetry model according to a type of the intermittent exercise activity.

18. The article of manufacture of claim 15, wherein the type of the intermittent exercise activity comprises at least one of cross-training, interval running, yoga, and weight lifting.

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

analyze the plurality of heart rate measurements to determine a first intensity level of the user during the period of time;

estimate that the user is experiencing the onset phase if the first intensity level of the user is greater than a first threshold intensity level; and

estimate that the user is experiencing the cool-down phase if the first intensity level of the user is less than the first threshold intensity level.

20. The article of manufacture of claim 15, wherein the cool-down calorimetry model comprises a fast-decay calorimetry model and a slow-decay calorimetry model.