IP Library › Granted Patent US 12,530,088
Granted Patent B2
US 12,530,088 · App. 18/978,713 · Granted Jan 20, 2026

Low-power hand-tracking system for wearable device

Inventors: Alex Feinman (San Mateo, CA); Ashwani Arya (Cypress, CA)
Assignee: Snap Inc.
G06F3/017G01S13/08G06F3/011
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Quick Facts
Patent No.
US 12,530,088
App. No.
18/978,713
Filed
Dec 12, 2024
Granted
Jan 20, 2026
Kind
B2
Art Unit
2627
USPC
345/156
Abstract

A method for a low-power hand-tracking system is described. In one aspect, a method includes polling a proximity sensor of a wearable device to detect a proximity event, the wearable device includes a low-power processor and a high-power processor, in response to detecting the proximity event, operating a low-power hand-tracking application on the low-power processor based on proximity data from the proximity sensor, and ending an operation of the low-power hand-tracking application in response to at least one of: detecting and recognizing a gesture based on the proximity data, detecting without recognizing the gesture based on the proximity data, or detecting a lack of activity from the proximity sensor within a timeout period based on the proximity data.

Claims (53)

1 . A method comprising:

operating a low-power gesture recognition application executed on a low-power processor of a wearable device;

determining that the low-power gesture recognition application fails to recognize a gesture; and

in response to determining that that the low-power gesture recognition application fails to recognize the gesture, activating a high-power gesture recognition application executed on a high-power processor of the wearable device.

2 . The method of claim 1 , wherein operating the low-power gesture recognition application further comprises:

executing a gesture detection and recognition routine using the low-power processor.

3 . The method of claim 1 , further comprising:

detecting a proximity event based on proximity sensor data from a proximity sensor of the wearable device,

wherein operating the low-power gesture recognition application is in response to detecting the proximity event.

4 . The method of claim 1 , further comprising:

generating a level of confidence of the gesture based on a gesture dictionary, using a gesture recognition algorithm operating on the low-power processor; and

identifying the gesture in response to the level of confidence exceeding a preset threshold.

5 . The method of claim 1 , further comprising:

identifying an operation corresponding to the gesture based on the high-power gesture recognition application; and

requesting the high-power processor to perform the operation.

6 . The method of claim 1 , further comprising:

identifying an operation of a mixed reality application corresponding to the gesture; and

requesting the high-power processor to perform the operation of the mixed reality application.

7 . The method of claim 1 , further comprising:

returning the wearable device to an idle state that polls a proximity sensor of the wearable device at a regular interval after the high-power gesture recognition application recognizes the gesture.

8 . The method of claim 1 , wherein the low-power processor comprises a microcontroller, wherein the high-power processor comprises at least one system-on-chip (SoC).

9 . The method of claim 1 , wherein the low-power processor is configured to only operate a proximity sensor of the wearable device and the low-power gesture recognition application, wherein the high-power processor is configured to operate all sensors of the wearable device.

10 . The method of claim 1 , wherein the low-power gesture recognition application uses a neural network to detect and recognize a hand gesture of a user of the wearable device based on proximity data from a proximity sensor of the wearable device, the neural network being configured to recognize a first set of hand gestures, and

wherein the high-power processor comprises a high-power hand-tracking application configured to recognize a second set of hand gestures using camera data from a higher resolution camera of the wearable device, the second set of hand gestures being larger than the first set of hand gestures.

11 . A wearable device comprising:

a low-power processor;

a high-power processor; and

a memory storing instructions that, when executed by one of the low-power processor or the high-power processor, configure the wearable device to perform operations comprising:

operating a low-power gesture recognition application executed on the low-power processor;

determining that the low-power gesture recognition application fails to recognize a gesture; and

in response to determining that that the low-power gesture recognition application fails to recognize the gesture, activating a high-power gesture recognition application executed on the high-power processor.

12 . The wearable device of claim 11 , wherein operating the low-power gesture recognition application further comprises:

executing a gesture detection and recognition routine using the low-power processor.

13 . The wearable device of claim 11 , wherein the operations further comprise:

detecting a proximity event based on proximity sensor data from a proximity sensor of the wearable device,

wherein operating the low-power gesture recognition application is in response to detecting the proximity event.

14 . The wearable device of claim 11 , wherein the operations further comprise:

generating a level of confidence of the gesture based on a gesture dictionary, using a gesture recognition algorithm operating on the low-power processor; and

identifying the gesture in response to the level of confidence exceeding a preset threshold.

15 . The wearable device of claim 11 , wherein the operations further comprise:

identifying an operation corresponding to the gesture based on the high-power gesture recognition application; and

requesting the high-power processor to perform the operation.

16 . The wearable device of claim 11 , wherein the operations further comprise:

identifying an operation of a mixed reality application corresponding to the gesture; and

requesting the high-power processor to perform the operation of the mixed reality application.

17 . The wearable device of claim 11 , wherein the operations further comprise:

returning the wearable device to an idle state that polls a proximity sensor of the wearable device at a regular interval after the high-power gesture recognition application recognizes the gesture.

18 . The wearable device of claim 11 , wherein the low-power processor comprises a microcontroller, wherein the high-power processor comprises at least one system-on-chip (SoC).

19 . The wearable device of claim 11 , wherein the low-power processor is configured to only operate a proximity sensor of the wearable device and the low-power gesture recognition application, wherein the high-power processor is configured to operate all sensors of the wearable device.

20 . A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to:

operating a low-power gesture recognition application executed on a low-power processor of a wearable device;

determining that the low-power gesture recognition application fails to recognize a gesture; and

in response to determining that that the low-power gesture recognition application fails to recognize the gesture, activating a high-power gesture recognition application executed on a high-power processor of the wearable device.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Dec 20, 2024
From: FEINMAN, ALEX; ARYA, ASHWANI
To: SNAP INC.
Reel/Frame 069651/0199 →
Continuity (2)
Continuation 17851465 · Jun 28, 2022
Related Publication 20250110573A1 · Apr 3, 2025
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