TERRAIN TYPE INFERENCE FROM WEARABLE WITH MOTION SENSING

• US 20160058372A1
• 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:
  US14/502,781
• Publication Number:
  US 20160058372A1
• 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 at least one sensor of a fitness tracking device, motion data of a user of the fitness tracking device; separating, by the fitness tracking device, the motion data into at least a first frequency signature attributable to movement by the user and a second frequency signature attributable to a type of a terrain on which the user is moving; determining, by the fitness tracking device, the type of the terrain on which the user is moving by analyzing the first frequency signature and the second frequency signature; and estimating, by the fitness tracking device, a rate of energy expenditure of the user by applying a calorimetry model including a coefficient or a parameter associated with the type of the terrain.

Claims

1. A method comprising:

obtaining, by at least one sensor of a fitness tracking device, motion data of a user of the fitness tracking device;

separating, by the fitness tracking device, the motion data into at least a first frequency signature attributable to movement by the user and a second frequency signature attributable to a type of a terrain on which the user is moving;

determining, by the fitness tracking device, the type of the terrain on which the user is moving by analyzing the first frequency signature and the second frequency signature; and

estimating, by the fitness tracking device, a rate of energy expenditure of the user by applying a calorimetry model comprising a coefficient or a parameter associated with the type of the terrain.

2. The method of claim 1, comprising:

determining, by the fitness tracking device, that the user is cycling;

determining, by the fitness tracking device, a coefficient of rolling resistance associated with the type of the terrain; and

updating, by the fitness tracking device, the calorimetry model to include the coefficient of rolling resistance.

3. The method of claim 1, wherein separating the motion data comprises:

applying, by the fitness tracking device, a low-pass filter to the motion data to obtain the first frequency signature attributable to movement by the user;

determining, by the fitness tracking device, residual harmonics at higher frequencies derivable from the first frequency signature; and

obtaining, by the fitness tracking device, the second frequency signature attributable to the type of the terrain by applying a high-pass filter to the motion data and by subtracting the residual harmonics derivable from the first frequency signature.

4. The method of claim 1, wherein the second frequency signature indicates that the type of the terrain is pavement.

5. The method of claim 1, wherein the second frequency signature indicates that the type of the terrain is gravel.

6. The method of claim 1, wherein the fitness tracking device is configured to be worn on a wrist of the user.

7. The method of claim 1, wherein the second frequency signature attributable to the type of the terrain includes a vibration pattern associated with the type of the terrain.

8. A fitness tracking device comprising:

a motion sensor; and

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

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

separate the motion data into at least a first frequency signature attributable to movement by the user and a second frequency signature attributable to a type of a terrain on which the user is moving;

determine the type of the terrain on which the user is moving by analyzing the first frequency signature and the second frequency signature; and

estimate a rate of energy expenditure of the user by applying a calorimetry model comprising a coefficient or a parameter associated with the type of the terrain.

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

determine that the user is cycling;

determine a coefficient of rolling resistance associated with the type of the terrain; and

update the calorimetry model to include the coefficient of rolling resistance.

10. The fitness tracking device of claim 8, comprising:

a low-pass filter for filtering the motion data; and

a high-pass filter for filtering the motion data,

wherein the processor configured to separate the motion data is configured to;

obtain, from the low-pass filter, the first frequency signature attributable to movement by the user;

determine residual harmonics at higher frequencies derivable from the first frequency signature; and

obtain the second frequency signature attributable to the type of the terrain by applying the high-pass filter to the motion data and by subtracting the residual harmonics derivable from the first frequency signature.

11. The fitness tracking device of claim 8, wherein the second frequency signature indicates that the type of the terrain is pavement.

12. The fitness tracking device of claim 8, wherein the second frequency signature indicates that the type of the terrain is gravel.

13. The fitness tracking device of claim 8, wherein the fitness tracking device is configured to be worn on a wrist of the user.

14. The fitness tracking device of claim 8, wherein the second frequency signature attributable to the type of the terrain includes a vibration pattern associated with the type of the terrain.

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 motion sensor and thereby cause the processor to operate to;

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

separate the motion data into at least a first frequency signature attributable to movement by the user and a second frequency signature attributable to a type of a terrain on which the user is moving;

determine the type of the terrain on which the user is moving by analyzing the first frequency signature and the second frequency signature; and

estimate a rate of energy expenditure of the user by applying a calorimetry model comprising a coefficient or a parameter associated with the type of the terrain.

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

determine that the user is cycling;

determine a coefficient of rolling resistance associated with the type of the terrain; and

update the calorimetry model to include the coefficient of rolling resistance.

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

apply a low-pass filter to the motion data to obtain the first frequency signature attributable to movement by the user;

determine residual harmonics at higher frequencies derivable from the first frequency signature; and

obtain the second frequency signature attributable to the type of the terrain by applying a high-pass filter to the motion data and by subtracting the residual harmonics derivable from the first frequency signature.

18. The article of manufacture of claim 15 wherein the second frequency signature indicates that the type of the terrain is pavement.

19. The article of manufacture of claim 15, wherein the second frequency signature indicates that the type of the terrain is gravel.

20. The article of manufacture of claim 15, wherein the fitness tracking device is configured to be worn on a wrist of the user.