Gesture recognition

Patent metadata

Field	Value
Publication number	US-11513610-B2
Application number	US-202117564392-A
Country	US
Kind code	B2
Filing date	Dec 29, 2021
Priority date	Aug 7, 2013
Publication date	Nov 29, 2022
Grant date	Nov 29, 2022

Abstract

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

First claim

What is claimed is:

1. A method comprising:
causing, by a device comprising a processor, gesture training to recognize a gesture, wherein the gesture training comprises:
sampling, at a plurality of different sampling rates, first raw sensor data associated with performance of a training gesture;
identifying, from the sampled first raw sensor data, one or more characteristics of the training gesture;
identifying, from the sampled first raw sensor data, a sampling rate, of the plurality of different sampling rates, that is lower than an upper sampling rate, of the plurality of different sampling rates, and at which the one or more characteristics of the training gesture are recognized as if sampled at the upper sampling rate; and
storing the one or more characteristics of the training gesture as a gesture sample for a first gesture and storing the lower sampling rate together with the gesture sample, wherein the stored gesture sample is one of a plurality of stored gesture samples;

after causing the gesture training, receiving second raw sensor data associated with movement of a user;
recognizing, from a first portion of the second raw sensor data, a first activity being performed by the user;
recognizing, from a second portion of the second raw sensor data and based on comparing one or more characteristics of the second portion to the plurality of stored gesture samples, the first gesture;
selecting, based on recognizing the first activity and the first gesture, an operational mode of the device that samples data at the lower sampling rate; and
after selecting the operational mode, sampling additional raw sensor data, during performance of the first activity, at the lower sampling rate.

2. The method of claim 1 , further comprising recognizing the first gesture further based on one or more of:
a motion pattern,
a pattern of touches of the device,
an orientation of the device, or
a proximity of the device to a beacon.

3. The method of claim 2 , wherein the device is a first sensor device, and the beacon is associated with a second device worn by a second user.

4. The method of claim 2 , further comprising:
registering, based on the proximity of the device to the beacon, the device with a location associated with the beacon.

5. The method of claim 2 , further comprising:
updating, based on the proximity of the device to the beacon, a progress time associated with the recognized first activity.

6. The method of claim 1 , further comprising recognizing the first activity based on: calculating, based on the first portion of the second raw sensor data, a quantity of steps taken by the user.

7. The method of claim 1 , further comprising:
storing, together with the gesture sample, one or more instructions that cause the device to execute one or more processes when the gesture sample is recognized.

8. The method of claim 1 , further comprising adjusting, based on recognizing a second gesture:
a quantity of sensors from which to receive the second raw sensor data or the additional raw sensor data, or
a type of sensor from which to receive the second raw sensor data or the additional raw sensor data.

9. The method of claim 1 , further comprising:
receiving the first raw sensor data and the second raw sensor data from one or more sensors associated with the device, when the device is worn on an appendage of the user, and
wherein the movement of the user comprises a motion of the appendage of the user.

10. The method of claim 1 , further comprising:
receiving the first raw sensor data and the second raw sensor data from one or more sensors associated with the device,
wherein the one or more sensors comprise one or more of: an accelerometer, a gyroscope, a force sensor, a magnetic field sensor, a global positioning system sensor, or a capacitance sensor.

11. An apparatus comprising:
one or more processors, and
memory storing instructions that, when executed by the one or more processors, cause the apparatus to:
cause gesture training to recognize a gesture by causing the apparatus to:
sample, at a plurality of different sampling rates, first raw sensor data associated with performance of a training gesture;
identify, from the sampled first raw sensor data, one or more characteristics of the training gesture;
identify, from the sampled first raw sensor data, a sampling rate, of the plurality of different sampling rates, that is lower than an upper sampling rate, of the plurality of different sampling rates, and at which the one or more characteristics of the training gesture are recognized as if sampled at the upper sampling rate; and
store the one or more characteristics of the training gesture as a gesture sample for a first gesture and storing the lower sampling rate together with the gesture sample, wherein the stored gesture sample is one of a plurality of stored gesture samples;

after causing the gesture training, receive second raw sensor data associated with movement of a user;
recognize, from a first portion of the second raw sensor data, a first activity being performed by the user;
recognize, from a second portion of the second raw sensor data and based on comparing one or more characteristics of the second portion to the plurality of stored gesture samples, the first gesture;
select, based on recognizing the first activity and the first gesture, an operational mode of the apparatus that samples data at the lower sampling rate; and
after selecting the operational mode, sample additional raw sensor data, during performance of the first activity, at the lower sampling rate.

12. The apparatus of claim 11 , wherein the instructions, when executed by the one or more processors, cause the apparatus to recognize the first gesture further based on a proximity of the apparatus to a beacon,
wherein the apparatus is a first sensor device, and
wherein the beacon is associated with a second device worn by a second user.

13. The apparatus of claim 11 , wherein the instructions, when executed by the one or more processors, cause the apparatus to:
recognize the first gesture further based on a proximity of the apparatus to a beacon; and
based on the proximity of the apparatus to the beacon:
register the apparatus with a location associated with the beacon, or
update a progress time associated with the recognized first activity.

14. The apparatus of claim 11 , wherein the instructions, when executed by the one or more processors, cause the apparatus to, based on recognizing a second gesture, adjust:
a quantity of sensors from which to receive the second raw sensor data or the additional raw sensor data, or
a type of sensor from which to receive the second raw sensor data or the additional raw sensor data.

15. The apparatus of claim 11 , wherein the instructions, when executed by the one or more processors, cause the apparatus to:
receive the first raw sensor data and the second raw sensor data from one or more sensors associated with the apparatus,
wherein the one or more sensors comprise one or more of: an accelerometer, a gyroscope, a force sensor, a magnetic field sensor, a global positioning system sensor, or a capacitance sensor.

16. A non-transitory, computer-readable medium storing instructions that, when executed by a device comprising a processor, cause the device to:
cause gesture training to recognize a gesture by causing the device to:
sample, at a plurality of different sampling rates, first raw sensor data associated with performance of a training gesture;
identify, from the sampled first raw sensor data, one or more character

Assignees

• Nike Inc

Inventors

• Goel Manan
• Cummings Kate
• Laigar Peter
• Switzer David
• Imlay Sebastian
• Lapinsky Michael

Classifications

• G06V10/147

  Details of sensors, e.g. sensor lenses (fingerprint or palmprint sensors G06V40/13; vascular sensors G06V40/145; eye sensors G06V40/19) · CPC title

• G06F1/163

  Wearable computers, e.g. on a belt · CPC title

• G06V40/23

  Recognition of whole body movements, e.g. for sport training · CPC title

• Y02D30/50

  in wire-line communication networks, e.g. low power modes or reduced link rate · CPC title

• G06F3/014

  Hand-worn input/output arrangements, e.g. data gloves · CPC title