A Deep Learning Approach for Automated Muscle Identification from sEMG Signals

Abstract

This study presents a supervised deep-learning approach for muscle identification using surface Electromyography (sEMG). We propose an optimized hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model to identify seven lower-limb muscles from only sEMG data, while addressing the inter-subject variability challenge. Using the publicly available ENABL3S dataset, we preprocess raw sEMG signals by detecting muscle bursts, removing overlaps, and zero-centering samples. Our model underwent iterative optimisation for both intrapersonal and interpersonal validation, achieving 97% and 93% accuracy, respectively. The results demonstrate improved feature extraction and generalization, advancing sEMG-based rehabilitation technologies.