A Deep Spatio-Temporal Model for Decoding Simultaneous and Continuous Hand Movements from Surface Electromyography

Abstract

Predicting continuous finger kinematics from surface electromyography (sEMG) signals provides crucial input for the intuitive proportional control of extreme upper-limb robotic prostheses. However, this regression task remains a complex challenge due to the noisy and non-linear dynamics of muscle activations. In this study, a hybrid deep spatio-temporal model is proposed that combines a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network to predict simultaneous finger joint angles from raw sEMG signals. Evaluated on 27 subjects from the Ninapro DB1 dataset using a rigorous repetition-wise split, the model was tested across three input window sizes (400ms, 600ms, and 800ms). Results demonstrate that the Hybrid CNN-LSTM consistently outperforms standard LSTM and Bidirectional LSTM (BiLSTM) baselines. The optimal 800ms window achieved the best overall performance (R2 = 0.7733, PCC =0.8841, RMSE =11.65°), demonstrating high-fidelity tracking particularly in the highly active digits. These findings highlight the feasibility of deploying efficient, single-modality deep learning estimators for robust, real-time prosthetic control without requiring complex multimodal sensor fusion.