IP Library Granted Patent US 12,429,956
Granted Patent B1
US 12,429,956 · App. 18/604,361 · Granted Sep 30, 2025

Method and system to detect two-hand gestures

Inventors: Enrico Rosario Alessi (Catania, IT); Alessandro Maio (Syracuse, IT); Fabio Passaniti (Syracuse, IT)
Assignee: STMicroelectronics International N.V.
G06F3/017G06F3/015G06F3/0346
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,429,956
App. No.
18/604,361
Granted
Sep 30, 2025
Kind
B1
Abstract

A wearable device includes an accelerometer, at least three electrodes to detect electromyography signal, a communication circuitry, and a processor circuitry. A user wears a primary and a secondary wearable device on two limbs. When the user performs a two-hand gesture, the wearable devices detect a primary and a secondary movement pattern. The primary device recognizes the two-hand gesture from the primary and secondary movement pattern.

Claims (70)

1. A method to detect hand gestures, comprising:

receiving a first movement pattern by a first wearable device, the first wearable device, wherein receiving the first movement pattern by the first wearable device includes:

receiving a first plurality of acceleration data from a first accelerometer over a time period, the accelerometer being on the first wearable device; and

receiving a first plurality of electromyography (EMG) signals from a first EMG sensor on the first wearable device over the time period;

receiving a second movement pattern by a second wearable device, the second wearable device, wherein receiving the second movement pattern by the second wearable device includes:

receiving a second plurality of acceleration data from a second accelerometer on the second wearable device over the time period; and

receiving a second plurality of EMG signals from a second EMG sensor on the second wearable device over the time period;

transmitting the second movement pattern from the second wearable device to the first wearable device; and

detecting a gesture by the first wearable device, including:

matching a gesture from the gesture database with the first movement pattern and the second movement pattern, the matching including:

determining if the first movement pattern or the second movement pattern is invalid;

generating a fill movement pattern in response to determining at least one of the first movement pattern and the second movement pattern is invalid; and

replacing the one invalid movement pattern with the fill movement pattern.

2. The method of claim 1 , wherein transmitting the second movement pattern from the second wearable device to the first wearable device uses body contact transmission.

3. The method of claim 1 , further comprising:

generating a first analog signal from the first plurality of EMG signals, and

generating a second analog signal from the second plurality of EMG signals.

4. The method of claim 3 , wherein the first EMG sensor includes at least three electrodes, and the second EMG sensor includes at least three electrodes.

5. The method of claim 4 , further comprising:

processing the first analog signal to determine contraction or relaxation of a first hand, and

processing the second analog signal to determine contraction or relaxation of a second hand.

6. The method of claim 1 , wherein receiving a movement pattern further comprises:

validating the plurality of acceleration data from the accelerometer.

7. The method of claim 1 , wherein the matching algorithm is Dynamic Time Warping.

8. A system, comprising:

a first wearable device, configured to be worn by a user on a first limb, wherein the first wearable device includes:

a first accelerometer, the first accelerometer configured to detect acceleration in at least three axes;

at least three first electrodes, the first electrode configured to detect electromyography (EMG) signal;

a first processor circuitry,

a first memory, and

a first communication circuitry; and

a second wearable device, configured to be worn by the user on a second limb, the second wearable device being configured to:

detect a second limb movement of the second limb, and

transmit a second limb movement pattern to the first wearable device, the first wearable device begin configured to detect a gesture with the first processor circuitry in response to the second limb movement, wherein the first limb is a first arm of the user and the second limb is a second arm of the user, and the first wearable device is configured to generate a first analog signal from EMG signals detected by the at least three first electrodes over a time period with a first analog to digital converter, wherein the second limb movement pattern comprises:

a plurality of acceleration data over the time period, and

a second analog signal generated from EMG signals detected by at least three second electrodes over the time period, the first analog signal representing a first contraction or relaxation of a first hand of the user; and the second analog signal representing a second contraction or relaxation of a second hand of the user;

wherein the first wearable device is further configured to:

validate a first plurality of acceleration data detected by its accelerometer;

validate a second plurality of acceleration data included in the second limb movement pattern; and

if one of the first plurality of acceleration data or the second plurality of acceleration data is invalid:

generate a fill acceleration data; and

replace the one invalid plurality of acceleration data with the fill acceleration data.

9. The system of claim 8 , wherein the first communication circuitry includes body contact communication.

10. The system of claim 8 , wherein the first wearable device is configured to detect a gesture by performing:

executing, on the first processor circuitry, a matching algorithm to a gesture database, the gesture database being on the first memory; and

matching a gesture from the gesture database with the first plurality of acceleration data and the second plurality of acceleration data.

11. The system of claim 10 , wherein the matching algorithm is Dynamic Time Warping.

12. The system of claim 11 , wherein the first wearable device is configured to detect a gesture by further performing:

validating the matched gesture with the first analog signal and the second analog signal.

13. The system of claim 12 , wherein the first processor circuitry, the first communication circuitry, the first accelerometer, and the first analog digital converter are made on a single silicon die.

14. A system, comprising:

a first wearable device;

a second wearable device that includes:

an accelerometer;

at least three electrodes configured to detect electromyography signal;

a processing circuitry, and

a communication circuitry, the second wearable device configured to:

prepare a first movement pattern from a plurality of acceleration data from the accelerometer over a time period and a plurality of EMG signals from the at least three electrodes over the time period;

receive a second movement pattern from the first wearable device; and

match a gesture from a gesture database with the first movement pattern and the second movement pattern;

validate the first movement pattern;

validate the second movement pattern; and

if one of the first movement pattern or second movement pattern is invalid:

generate a fill movement pattern; and

replace the one invalid movement pattern with the fill movement pattern.

15. The system of claim 14 , wherein the communication circuitry includes body contact communication.

16. The system of claim 14 , wherein the first movement pattern further includes:

an analog signal generated from the at least three electrodes, the analog signal representing a contraction or relaxation of a hand of a user.

17. The wearable device of claim 16 , comprising:

an analog digital converter to generate the analog signal, wherein the processor circuitry, the communication circuitry, the accelerometer, and the analog digital converter are made on a single silicon die.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 18, 2024
From: STMICROELECTRONICS S.R.L.
To: STMICROELECTRONICS INTERNATIONAL N.V.
Reel/Frame 068434/0001 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Mar 28, 2024
From: ALESSI, ENRICO ROSARIO; MAIO, ALESSANDRO; PASSANITI, FABIO
To: STMICROELECTRONICS S.R.L.
Reel/Frame 066938/0888 →
References Cited (25)
US 20070124503A1 · Ramos · 2007 [cited by examiner]
US 20090326406A1 · Tan · 2009 [cited by examiner]
US 20110175879A1 · Tanaka · 2011 [cited by examiner]
US 20120268376A1 · Bi · 2012 [cited by examiner]
US 20130201164A1 · Omori · 2013 [cited by examiner]
US 20150070270A1 · Bailey et al. · 2015 [cited by applicant]
US 20150370320A1 · Connor · 2015 [cited by applicant]
US 20170285756A1 · Wang et al. · 2017 [cited by applicant]
US 20200310539A1 · Barachant et al. · 2020 [cited by applicant]
US 20220269346A1 · Hussami et al. · 2022 [cited by applicant]
US 20230019413A1 · Stern et al. · 2023 [cited by applicant]
CN 110362190A · 2019 [cited by applicant]
Chung et al., “Waveform reliability with different recording electrode placement in facial electroneuronography,” [cited by applicant]
Ghani et al., “Evaluation of Portable Potentiostats for Electrochemical Measurements: Voltammetry and Impedance Spectroscopy,” 2022 IEEE 8th International Conference on Smart Instrumentation, Measurement and Application… [cited by applicant]
Morikawa et al., “Compact Wireless EEG System with Active Electrodes for Daily Healthcare Monitoring,” 2013 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, Jan. 11-14, 2013, pp. 204-205… [cited by applicant]
Rassaei et al., “Lactate biosensors: current status and outlook,” [cited by applicant]
Reinschmidt et al., “Realtime Hand-Gesture Recognition Based on Novel Charge Variation Sensor and IMU,” 2022 IEEE Sensors, Dallas, TX, USA, Oct. 30- Nov. 2, 2022. (4 pages). [cited by applicant]
Shin et al., “Controlling Mobile Robot Using IMU and EMG Sensor-based Gesture Recognition,” 2014 Ninth International Conference on Broadband and Wireless Computing, Communication and Applications, Guangdong, China, Nov.… [cited by applicant]
Wang et al., “EMG-based Hand Gesture Recognition by Deep Time-frequency Learning for Assisted Living & Rehabilitation,” 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON),… [cited by applicant]
Wang et al., “Equivalent Impedance Models for Electrochemical Nanosensor-Based Integrated System Design,” [cited by applicant]
Yoshikawa et al., “Real-Time Hand Motion Estimation Using EMG Signals with Support Vector Machines,” SICE-ICASE International Joint Conference, Busan, South Korea, Oct. 18-21, 2006, pp. 593-598. (6 pages). [cited by applicant]
Sun et al., “Gesture recognition algorithm based on multi-scale feature fusion in RGB-D images,” The Institution of Engineering and Technology, Dec. 23, 2022. (27 pages). [cited by applicant]
Manoni et al., “ Long-Term Polygraphic Monitoring through MEMS and Charge Transfer for Low-Power Wearable Applications,” Sensors, Mar. 27, 2022. (19 pages). [cited by applicant]
STMicroelectronics, “Qvar sensing,” Application note, AN5755, Rev 4, Aug. 2022, pp. 1-29. [cited by applicant]
Kim et al., “Secure communication system for wearable devices wireless intra body communication,” IEEE International Conference on Consumer Electronics, Jan. 9, 2015, pp. 381-382. [cited by applicant]