A Graph Neural Network Model for Real-Time Gesture Recognition Based on sEMG Signals

2026-07-08

AI Summary

This work proposes a novel approach to high-accuracy, low-latency hand gesture recognition for real-time control in intelligent prosthetics and augmented reality by modeling surface electromyographic (sEMG) signals from the forearm as graph structures that capture muscle activation patterns. For the first time, graph neural networks are leveraged for efficient gesture classification, integrating sEMG signal processing, dynamic graph construction, and lightweight graph-based inference. Evaluated on eight subjects, the method achieves an average recognition accuracy of 99% with an end-to-end processing latency of only 48 ms on an M1 Pro CPU, substantially meeting the stringent demands of real-time interaction. These results underscore the innovative value and practical potential of graph-structured modeling in sEMG-based gesture recognition.

Abstract

For seemless control of advanced hand prostheses and augmented reality, accurate and immediate hand gestures recognition is essential. Surface electromyography (sEMG) signals obtained from the forearm are commonly employed for this purpose. In this paper, we present a novel approach for sEMG representation that utilizes graph networks which contain information about muscle activation patterns in the forearm. Based on these graph networks, we have developed a machine learning algorithm capable of real-time hand gesture recognition using a graph neural network. The algorithm's performance was evaluated using sEMG signals acquired from myoband, which has 8 electrodes placed around the forearm, involving 8 healthy subjects. The proposed method demonstrated an average classification accuracy of 99\%, surpassing the performance of state-of-the-art techniques. The average time for both graph construction and prediction stood at 48ms utilizing a M1 pro CPU, rendering the approach well-suited for real-time applications.

Problem

• gesture recognition
• sEMG
• real-time
• hand prostheses
• augmented reality

Innovation

• Graph Neural Network
• sEMG
• Real-Time Gesture Recognition
• Muscle Activation Patterns
• Hand Prostheses