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 (R 2 = 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.