GESTURE RECOGNITION

• US 20150046886A1
• Filed: 08/07/2014
• Published: 02/12/2015
• Est. Priority Date: 08/07/2013
• Status: Active Grant

• Current Assignee: Nike, Inc.
• Original Assignee: Nike, Inc.
• Inventors: Goel, Manan, Cummings, Kate, Switzer, David, Lapinsky, Michael, Laigar, Peter, Imlay, Sebastian
• Granted Patent: US 11,243,611 B2
• Time in Patent Office: Days
• US Class Current: 715/863
• CPC Class Codes: G06F 1/163 Wearable computers, e.g. on a belt
• CPC Class Codes: G06F 1/3231 Monitoring the presence, absence or movement of users
• CPC Class Codes: G06F 1/329 by task scheduling
• CPC Class Codes: G06F 3/011 Arrangements for interaction with the human body, e.g. for user immersion in virtual reality blind teaching G09B21/00
• CPC Class Codes: G06F 3/014 Hand-worn input/output arrangements, e.g. data gloves
• CPC Class Codes: G06F 3/017 Gesture based interaction, e.g. based on a set of recognized hand gestures interaction based on gestures traced on a digitiser G06F3/04883
• CPC Class Codes: G06V 10/147 Details of sensors, e.g. sensor lenses fingerprint or palmprint sensors G06V40/13; vascular sensors G06V40/145; eye sensors G06V40/19
• CPC Class Codes: G06V 40/23 Recognition of whole body movements, e.g. for sport training
• CPC Class Codes: G09B 19/0038 Sports
• CPC Class Codes: G16H 20/30 relating to physical therapies or activities, e.g. physiotherapy, acupressure or exercising
• CPC Class Codes: Y02D 10/00 Energy efficient computing, e.g. low power processors, power management or thermal management
• CPC Class Codes: Y02D 30/50 in wire-line communication networks, e.g. low power modes or reduced link rate

Abstract

A wrist-worn athletic performance monitoring system, including a gesture recognition processor configured to execute gesture recognition processes. Interaction with the performance monitoring system may be based, at least in part, on gestures performed by the user, and offer an alternative to making selections on the performance monitoring system using physical buttons, which may be cumbersome and/or inconvenient to use while performing an athletic activities. Additionally, recognized gestures may be used to select one or more operational modes for the athletic performance monitoring systems such that a reduction in power consumption may be achieved.

Claims

1. A computer-implemented method of operating a device comprising:

(a) receiving at a processor included in the device operating in a first operational mode and configured to be worn by a human acceleration data representing movement of an appendage of a user;

(b) classifying at a processor the acceleration data received in (a) as one of a plurality of gestures;

(c) based upon at least the classified gesture, entering a second operational mode of the device;

(d) receiving at a processor included in the device operating in the second operational mode acceleration data representing movement of an appendage of a user; and

(e) classifying at a processor the acceleration data received in (d) as one of a plurality of gestures.

2. The computer-implemented method of claim 1, wherein the processor in (a), the processor in (b), the processor in (d) and the processor in (e) are a common processor.

3. The computer-implemented method of claim 1, wherein the gesture is classified based on a motion pattern of the device.

4. The computer-implemented method of claim 1, wherein the gesture is classified based on a pattern of touches of the device by the user.

5. The computer-implemented method of claim 4, wherein the pattern of touches is a series of taps of the device by the user.

6. The computer-implemented method of claim 1, wherein the gesture is classified based on an orientation of the device.

7. The computer-implemented method of claim 1, wherein the gesture is classified based on a proximity of the device to a beacon.

8. The computer-implemented method of claim 7, wherein the device is a first sensor device, and the beacon is associated with a second device on a second user.

9. The computer-implemented method of claim 7, wherein the beacon is associated with a location, and the device is registered at the location based on the proximity of the device to the beacon.

10. The computer-implemented method of claim 1, further comprising:

comparing a first value of acceleration data obtained from the accelerometer against a plurality of threshold values;

determining that the first value of acceleration data corresponds to a first threshold value within the plurality the threshold values; and

wherein the classification of the acceleration data as a gesture is based upon the correspondence of the first value of acceleration data to the first threshold.

11. A unitary apparatus configured to be worn around an appendage of a user, comprising:

a sensor configured to capture acceleration data from the appendage of the user;

a processor;

a non-transitory computer-readable medium comprising computer-executable instructions that when executed by the processor perform at least;

receive the captured acceleration data at the processor;

classify, by the processor, the captured acceleration data as one of a plurality of gestures;

select an operational mode for the processor based on the classified gesture, wherein the processor samples data from the accelerometer based on the operational mode.

12. The unitary apparatus of claim 11, wherein the processor selects a sampling rate at which data is sampled from the accelerometer based on the classified gesture.

13. The unitary apparatus of claim 11, wherein the operational mode is a hibernation mode such that the processor uses a low level of power.

14. The unitary apparatus of claim 11, wherein the operational mode is further selected based on a recognized activity by the processor.

15. The unitary apparatus of claim 11, wherein the processor selects a second sensor from which to receive user motion data based on the classified gesture.

16. A non-transitory computer-readable medium comprising computer-executable instructions that when executed by a processor is configured to perform at least:

a) receiving, from a sensor of a device, motion data of a user;

b) identifying a gesture from the received motion data;

c) adjusting an operational mode of the device based on the gesture identified in b).

17. The non-transitory computer-readable medium of claim 16, wherein the sensor is one selected from a group comprising:

an accelerometer, a gyroscope, a force sensor, a magnetic field sensor, a global positioning system sensor, and a capacitance sensor.

18. The non-transitory computer-readable medium of claim 16, wherein the gesture is identified based on a motion pattern of the sensor device.

19. The non-transitory computer-readable medium of claim 16, wherein the gesture is identified based on a pattern of touches of the sensor device by the user.

20. The non-transitory computer-readable medium of claim 16, wherein the gesture is identified based on an orientation of the sensor device.