Sensor fusion approach to energy expenditure estimation

• US 9,918,646 B2
• Filed: 09/30/2014
• Issued: 03/20/2018
• Est. Priority Date: 09/02/2014
• Status: Active Grant

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: Singh Alvarado, Alexander, Pham, Hung A., Moore, III, Richard Channing, Raghuram, Karthik Jayaraman, Srinivas, Umamahesh, Tan, Xing
• Primary Examiner(s): Stice, Paula J
• Application Number:
  US14/502,754
• Publication Number:
  US 20160058371A1
• Time in Patent Office: 1,267 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 a plurality of heart rate measurements of the user over a period of time; obtaining motion data of the user over the period of time; analyzing the motion data of the user to determine for each of the plurality of heart rate measurements, a corresponding work rate measurement; determining, for each of the plurality of heart rate measurements, a first confidence level; determining, for each corresponding work rate measurement, a second confidence level; and estimating a first energy expenditure rate using the plurality of heart rate measurements; estimating a second energy expenditure rate using the plurality of work rate measurements; and estimating a weighted energy expenditure rate of the user by combining the first energy expenditure rate weighted by the first confidence level and the second energy expenditure rate weighted by the second confidence level.

Claims

1. A fitness tracking device configured to be worn on a user'"'"'s wrist, the device comprising:

a heart rate sensor, wherein the heart rate sensor is configured to measure a heart rate of the user;

a first motion sensor, wherein the first motion sensor is configured to measure motion of the user;

a display module configured to provide an output to the user; and

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

receive input indicating a start of an exercise session;

during the exercise session, obtain a plurality of heart rate measurements of the user over a period of time from the heart rate sensor;

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

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

determine, for each of the plurality of heart rate measurements, a first confidence level, wherein the first confidence level comprises a first variance associated with each heart rate measurement;

determine, for each corresponding work rate measurement, a second confidence level, wherein the second confidence level comprises a second variance associated with each corresponding work rate measurement; and

estimate a first energy expenditure rate using the plurality of heart rate measurements;

estimate a second energy expenditure rate using the plurality of corresponding work rate measurements;

estimate a weighted energy expenditure rate of the user by combining the first energy expenditure rate weighted by the first confidence level and the second energy expenditure rate weighted by the second confidence level; and

output an indication of the weighted energy expenditure rate of the user to the display module;

wherein the display module is configured to provide the indication of the weighted energy expenditure rate of the user as the output to the user.

2. The fitness tracking device of claim 1, wherein the processor configured to estimate the weighted energy expenditure rate is configured to apply a Bayesian probability framework using the first variance of the first confidence level and the second variance of the second confidence level.

3. The fitness tracking device of claim 1, wherein the processor configured to determine each corresponding work rate measurement is configured to:

determine an activity context of the user; and

determine a calorimetry model corresponding to the activity context.

4. The fitness tracking device of claim 3, wherein the activity context comprises cycling, and wherein the calorimetry model comprises a cycling calorimetry model.

5. The fitness tracking device of claim 4, wherein the cycling calorimetry model comprises at least one of a coefficient of rolling resistance, a mass of a bicycle, a mass of the user, a gravity, a slope of a surface, an air density, an aerodynamic drag, a moment of inertia of the bicycle, a radius of a wheel of the bicycle, a speed of the bicycle, and an acceleration of the bicycle.

6. The fitness tracking device of claim 5, wherein the aerodynamic drag comprises a coefficient of a first term of the cycling calorimetry model, wherein the speed of the bicycle comprises the speed of the user, and wherein the speed of the user comprises a variable of a second term of the cycling calorimetry model.

7. The fitness tracking device 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.