Graph Neural Network Achieves 99% Accuracy in Real-Time Gesture Recognition from sEMG

Jul 10, 2026

Researchers developed a graph neural network model for real-time hand gesture recognition using surface electromyography (sEMG) signals. The method achieved 99% average classification accuracy on data from 8 subjects using a Myoband, with graph construction and prediction averaging 48ms on an M1 Pro CPU.

Why it matters: This work shows that graph-based representations of muscle activation patterns can improve the speed and accuracy of sEMG gesture recognition, which is important for advanced prosthetics and augmented reality interfaces.

Full story at: arXiv AI/ML ↗