METHOD AND SYSTEM TO CALIBRATE FITNESS LEVEL AND DIRECT CALORIE BURN USING MOTION, LOCATION SENSING, AND HEART RATE

• US 20160058356A1
• Filed: 09/30/2014
• Published: 03/03/2016
• Est. Priority Date: 09/02/2014
• Status: Abandoned Application

• 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
• Application Number:
  US14/501,634
• Publication Number:
  US 20160058356A1
• 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

The present disclosure relates generally to improving calorie expenditure prediction and tracking and, more particularly, to techniques for calibration and calorimetry using data from motions sensors and heart rate sensors. Embodiments of the present disclosure include a fitness tracking device and techniques for accurately tracking an individual'"'"'s energy expenditure over time and over a variety of activities while wearing the fitness tracking device. In some embodiments, the fitness tracking device may be a wearable device. The wearable device may be worn on a wrist, such as a watch, and it may include one or more microprocessors, a display, and a variety of sensors, including a heart rate sensor and one or more motion sensors.

Claims

1. A method comprising:

determining, by a fitness tracking device, that a user of the fitness tracking device is engaged in an exercise session at an intensity level above a threshold intensity level;

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

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, for each of the plurality of heart rate measurements, a corresponding work rate measurement, wherein the work rate measurement comprises a speed of the user at a corresponding time;

estimating, by the fitness tracking device, for each of the plurality of heart rate measurements, a corresponding energy expenditure rate at the corresponding time using the corresponding work rate measurement;

estimating an aerobic capacity of the user by applying, by the fitness tracking device, a regression analysis of the plurality of heart rate measurements and the corresponding energy expenditure rate for each of the plurality of heart rate measurements; and

storing, by the fitness tracking device, the aerobic capacity of the user in a memory of the fitness tracking device.

2. The method of claim 1, wherein determining that the user of the fitness tracking device is engaged in the exercise session at the intensity level above the threshold intensity level comprises inferring an activity context of the user using the motion data of the user.

3. The method of claim 1, wherein the motion data of the user comprises a second plurality of motion measurements from a second motion sensor of a companion device in wireless communication with the fitness tracking device.

4. The method of claim 3, wherein the companion device comprises a smartphone, and wherein the second motion sensor of the companion device comprises a GPS sensor.

5. The method of claim 1, wherein estimating the aerobic capacity of the user comprises:

obtaining, from the memory of the fitness tracking device, an observed minimum heart rate;

obtaining, from the memory of the fitness tracking device, an observed maximum heart rate;

obtaining, by the fitness tracking device, an age of the user;

using the age of the user to determine, by the fitness tracking device, an estimated maximum heart rate; and

determining, for each heart rate measurement of the plurality of heart rate measurements, a corresponding intensity level by comparing each heart rate measurement with the observed minimum heart rate and a greater of the observed maximum heart rate and the estimated maximum heart rate.

6. The method of claim 1, wherein the regression analysis is a linear regression analysis, and wherein the linear regression analysis is constrained, by the fitness tracking device, to include a point representing a minimum energy expenditure rate of the user at a corresponding minimum heart rate of the user.

7. The method of claim 1, wherein each corresponding energy expenditure rate is estimated by the fitness tracking device using at least one of an age of the user, a weight of the user, and a sex of the user.

8. A fitness tracking device comprising:

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

a motion sensor for obtaining motion data of the user;

a memory; and

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

determine that the user is engaged in an exercise session at an intensity level above a threshold intensity level;

obtain a plurality of heart rate measurements of the user over a period of time during the exercise session, wherein the plurality of heart rate measurements comprises heart rate data from the heart rate sensor of the fitness tracking device;

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

determine, for each of the plurality of heart rate measurements, a corresponding work rate measurement, wherein the work rate measurement comprises a speed of the user at a corresponding time;

estimate, for each of the plurality of heart rate measurements, a corresponding energy expenditure rate at the corresponding time by using the corresponding work rate measurement;

estimate an aerobic capacity of the user by applying a regression analysis of the plurality of heart rate measurements and the corresponding energy expenditure rate for each of the plurality of heart rate measurements; and

storing the aerobic capacity of the user in the memory of the fitness tracking device.

9. The fitness tracking device of claim 8, wherein the processor is configured to infer an activity context of the user by using the motion data of the user.

10. The fitness tracking device of claim 8, wherein the motion data of the user comprises a second plurality of motion measurements from a second motion sensor of a companion device, wherein the companion device is in wireless communication with the processor.

11. The fitness tracking device of claim 10, wherein the companion device comprises a smartphone, and wherein the second motion sensor of the companion device comprises a GPS sensor.

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

obtain, from the memory of the fitness tracking device, an observed minimum heart rate;

obtain, from the memory of the fitness tracking device, an observed maximum heart rate;

obtain an age of the user;

use the age of the user to determine an estimated maximum heart rate; and

determine, for each heart rate measurement of the plurality of heart rate measurements, a corresponding intensity level, wherein the corresponding intensity level is determined by comparing each heart rate measurement with the observed minimum heart rate and a greater of the observed maximum heart rate and the estimated maximum heart rate.

13. The fitness tracking device of claim 8, wherein the regression analysis comprises a linear regression analysis, and wherein the processor is configured to constrain the linear regression analysis to include a point representing a minimum energy expenditure rate of the user at a corresponding minimum heart rate of the user.

14. The fitness tracking device of claim 8, wherein the processor is configured to estimate each corresponding energy expenditure rate using at least one of an age of the user, a weight of the user, and a sex of the user.

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 memory, a heart rate sensor for obtaining heart rate measurements from 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;

determine that the user is engaged in an exercise session at an intensity level above a threshold intensity level;

obtain a plurality of heart rate measurements of the user over a period of time during the exercise session, wherein the plurality of heart rate measurements comprises heart rate data from the heart rate sensor of the fitness tracking device;

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

determine, for each of the plurality of heart rate measurements, a corresponding work rate measurement, wherein the work rate measurement comprises a speed of the user at a corresponding time;

estimate, for each of the plurality of heart rate measurements, a corresponding energy expenditure rate at the corresponding time by using the corresponding work rate measurement;

estimate an aerobic capacity of the user by applying a regression analysis of the plurality of heart rate measurements and the corresponding energy expenditure rate for each of the plurality of heart rate measurements; and

storing the aerobic capacity of the user in the memory of the fitness tracking device.

16. The article of manufacture of claim 15, wherein the processor is caused to infer an activity context of the user by using the motion data of the user.

17. The article of manufacture of claim 15, wherein the motion data of the user comprises a second plurality of motion measurements from a second motion sensor of a companion device in wireless communication with the processor.

18. The article of manufacture of claim 17, wherein the companion device comprises a smartphone, and wherein the second motion sensor of the companion device comprises a GPS sensor.

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

obtain, from the memory of the fitness tracking device, an observed minimum heart rate;

obtain, from the memory of the fitness tracking device, an observed maximum heart rate;

obtain an age of the user;

use the age of the user to determine an estimated maximum heart rate; and

determine, for each heart rate measurement of the plurality of heart rate measurements, a corresponding intensity level by comparing each heart rate measurement with the observed minimum heart rate and a greater of the observed maximum heart rate and the estimated maximum heart rate.

20. The article of manufacture of claim 15, wherein the regression analysis is a linear regression analysis, and wherein the processor is caused to constrain the linear regression analysis to include a point representing a minimum energy expenditure rate of the user at a corresponding minimum heart rate of the user.