SYSTEMS AND METHODS OF SWIMMING CALORIMETRY

• US 20180056129A1
• Filed: 08/31/2017
• Published: 03/01/2018
• Est. Priority Date: 08/31/2016
• Status: Active Grant

• Current Assignee: Apple Inc.
• Original Assignee: Apple Inc.
• Inventors: NARASIMHA RAO, Bharath, MERMEL, Craig H., RAGHURAM, Karthik Jayaraman, PHAM, Hung A., OCHS, James P., MAJJIGI, Vinay R., SINGH ALVARADO, Alexander, CHOW, Sunny K., SRINIVAS, Umamahesh, TAN, Xing, GUERS, Robin T., ULLAL, Adeeti, JACKSON, Stephen P., AGARWAL, Mrinal
• Granted Patent: US 10,617,912 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• 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 5/02438 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 5/1114 Tracking parts of the body
• CPC Class Codes: A61B 5/1118 Determining activity level
• 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 5/4866 Evaluating metabolism A61B5/083 takes precedence
• CPC Class Codes: A61B 5/681 Wristwatch-type devices
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 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 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/7475 User input or interface means, e.g. keyboard, pointing device, joystick
• CPC Class Codes: A63B 2024/0071 Distinction between different activities, movements, or kind of sports performed
• CPC Class Codes: A63B 2208/03 the user being in water
• CPC Class Codes: A63B 2220/12 Absolute positions, e.g. by using GPS
• CPC Class Codes: A63B 2220/17 Counting, e.g. counting periodical movements, revolutions or cycles, or including further data processing to determine distances or speed
• CPC Class Codes: A63B 2220/30 Speed
• CPC Class Codes: A63B 2220/56 Pressure
• CPC Class Codes: A63B 2220/73 : Altitude
• CPC Class Codes: A63B 2220/74 : Atmospheric pressure
• CPC Class Codes: A63B 2220/803 : Motion sensors
• CPC Class Codes: A63B 2220/836 : Sensors arranged on the body of the user
• CPC Class Codes: A63B 2225/50 : Wireless data transmission, e.g. by radio transmitters or telemetry
• CPC Class Codes: A63B 2230/06 : heartbeat rate only
• CPC Class Codes: A63B 2230/75 : calorie expenditure
• CPC Class Codes: A63B 24/0006 : Computerised comparison for qualitative assessment of motion sequences or the course of a movement
• CPC Class Codes: A63B 24/0062 : Monitoring athletic performances, e.g. for determining the work of a user on an exercise apparatus, the completed jogging or cycling distance
• CPC Class Codes: G06F 2218/12 : Classification; Matching
• CPC Class Codes: G06V 40/23 : Recognition of whole body movements, e.g. for sport training
• CPC Class Codes: G06V 40/28 : Recognition of hand or arm movements, e.g. recognition of deaf sign language static hand signs G06V40/113
• CPC Class Codes: G16H 50/20 : for computer-aided diagnosis, e.g. based on medical expert systems

Abstract

The present disclosure relates to systems and methods of estimating energy expenditure of a user while swimming. A processor circuit of a user device can estimate a speed of the user based on a stroke rate and a stroke length. The processor circuit can estimate an efficiency of the user. The processor circuit can classify a swimming style of the user. The processor circuit can determine energy expenditure of the user based on the speed, the efficiency, and the style. The processor circuit can also detect glides of the user and adjust the energy expenditure.

Claims

1. A method for improving an accuracy of a wearable device while estimating energy expenditure of a user during a swimming session, the method comprising:

collecting, by a motion sensing module of a wearable device, motion data of the user;

determining, by a processor circuit of the wearable device, rotational data of the user based on the motion data;

counting, by the processor circuit, a number of strokes made by the user based on the set of rotational data;

estimating, by the processor circuit, a stroke rate of the user based on the number of strokes;

counting, by the processor circuit, a number of laps finished by the user based on the set of rotational data;

calculating, by the processor circuit, a stroke length based on the number of strokes, the number of laps finished, and a lap length associated with the swimming session;

calculating, by the processor circuit, a speed of the user based on the stroke rate and the stroke length;

estimating, by the processor circuit, an efficiency of the user based on at least one of the stroke length or a level of stroke orbit consistency of the user;

classifying, by the processor circuit, a swimming style of the user based on the set of rotational data;

determining, by the processor circuit, an energy expenditure of the user based on the speed, efficiency, and swimming style of the user; and

outputting, by the processor circuit, the determined energy expenditure of the user.

2. The method of claim 1, wherein the motion data is expressed in a first frame of reference and the rotation data is expressed in a second frame of reference.

3. The method of claim 1, comprising:

receiving, by the processor circuit, one or more inputs from the user, wherein the one or more inputs from the user comprise at least one of the user'"'"'s gender, age, height, weight, or body mass index;

adjusting, by the processor circuit, the energy expenditure of the user based on the received one or more inputs; and

outputting, by the processor circuit, the adjusted energy expenditure of the user.

4. The method of claim 1, comprising:

measuring, by a heart rate sensing module of the user wearable device, a heart rate of the user;

determining, by the processor circuit, a fraction of heart rate reserve (FHR) of the user based on the measured heart rate;

estimating, by the processor circuit, the user'"'"'s anaerobic energy expenditure based on at least one of the FHR, the stroke length, the stroke rate, or the speed; and

determining, by the processor circuit, the energy expenditure of the user based on the estimated anaerobic energy expenditure.

5. The method of claim 1, comprising:

measuring, by a heart rate sensing module of the wearable device, a heart rate of the user;

determining, by the processor circuit, a fraction of heart rate reserve (FHR) of the user based on the measured heart rate;

determining, by the processor circuit, a maximal oxygen uptake of the user; and

determining, by the processor circuit, a heart rate model based on FHR and the maximal oxygen uptake, wherein determining the energy expenditure of the user based on the heart rate model.

6. The method of claim 1, wherein calculating the speed of the user comprises:

receiving, by a GPS sensor of the wearable device, location data of the user during the swimming session; and

calculating, by the processor circuit, a stroke length and a speed of the user based on the received location data.

7. The method of claim 1, wherein the motion sensing module comprises at least one of a gyroscope or an accelerometer.

8. The method of claim 1, comprising:

detecting, by the processor circuit, one or more glides of the user during the swimming session; and

determining, by the processor circuit, the energy expenditure of the user based on the detected one or more glides.

9. The method of claim 8, wherein detecting the one or more glides of the user comprises:

determining, by the processor circuit, whether the user is making a turn based on the set of rotational data;

detecting, by the processor circuit, a pose angle of the user'"'"'s wrist based on the set of rotational data;

determining, by the processor circuit, whether the user is under water using pressure data received from a pressure sensor of the wearable device; and

detecting, by the processor circuit, one or more glides of the user based on the stroke rate, the pose of the user'"'"'s wrist, whether the user is making a turn, and whether the user is under water.

10. The method of claim 9, wherein detecting a pose angle of the user'"'"'s wrist comprises:

determining, by the processor circuit, a direction of gravity relative to the user device based on the motion data;

detecting, by the processor circuit, a pose angle of the user'"'"'s wrist based on the direction of gravity.

11. The method of claim 9, wherein determining whether the user is under water comprises:

determining, by the processor circuit, an elevation of the wearable device based on the pressure data; and

determining, by the processor circuit, whether the user is under water based on the determined elevation.

12. A system for improving an accuracy of a wearable device while estimating energy expenditure of a user during a swimming session, the system comprising:

a motion sensing module configured to collect motion data of the user;

a processor circuit in communication with the motion sensing module and configured to execute instructions causing the processor circuit to;

determine rotational data of the user base on the motion data;

count a number of strokes made by the user based on the set of rotational data;

estimate a stroke rate of the user based on the number of strokes;

count a number of laps finished by the user based on the set of rotational data;

estimate a stroke length based on the number of strokes, the number of laps finished, and a lap length associated with the swimming session;

estimate a speed of the user based on the stroke rate and the stroke length;

estimate efficiency of the user based on at least one of the stroke length or a level of stroke orbit consistency of the user;

classify a swimming style of the user based on the set of rotational data;

determine an energy expenditure of the user based on the speed, efficiency, and swimming style of the user; and

output the determined energy expenditure of the user.

13. The system of claim 12, wherein the instructions further cause the processor circuit to:

receive one or more inputs from the user, wherein the one or more inputs from the user comprise at least one of the user'"'"'s gender, age, height, weight, or body mass index;

adjust the determined energy expenditure of the user based on the received one or more inputs; and

output the adjusted energy expenditure of the user.

14. The system of claim 12, further comprising a heart rate sensing module configured to measure a heart rate of the user.

15. The system of claim 14, wherein the instructions further cause the processor circuit to:

determine, a fraction of heart rate reserve (FHR) of the user based on the measured heart rate;

determine, a maximal oxygen uptake of the user;

determine, a heart rate model based on the FHR and the maximal oxygen uptake; and

determine, the energy expenditure of the user based on the heart rate model.

16. The system of claim 12, further comprising a GPS sensor configured to receive location data of the user.

17. The system of claim 16, wherein the instructions further cause the processor circuit to:

estimate a stroke length and a speed of the user based on the received location data of the user.

18. The system of claim 12, wherein the motion sensing module comprises at least one of a gyroscope or an accelerometer.

19. The system of claim 12, wherein the instructions further cause the processor circuit to:

determine whether the user is making a turn based on the set of rotational data;

detect a pose of the user'"'"'s wrist based on the set of rotational data;

determine whether the user is under water based on pressure data received from a pressure sensor of the wearable device;

determine whether the user is gliding while swimming based on the stroke rate, the pose of the user'"'"'s wrist, whether the user is making a turn, and whether the user is under water;

adjust the energy expenditure of the user based on the determined glide; and

output the adjusted energy expenditure of the user.