A medium-density EMG system for real-time proportional and simultaneous neuroprosthetic hand control using lightweight neural networks
Objective. Restoring intuitive and natural hand control remains a key challenge in neuroprosthetics. A promising approach is to decode continuous finger-joint angles from electromyography (EMG) signals, enabling dexterous interaction. Deep neural networks show strong potential for this task, but are often too computationally demanding for embedded deployment and are rarely validated in real-time. Approach. This study presents a unified two-phase framework for real-time decoding of 11 finger-joint angles from medium-density EMG signals. The framework integrates preprocessing selection, architecture optimization, and deployment-related constraints. A convolutional neural network (CNN) baseline and an adapted version of the dual predictive attractor-refinement strategy (DPARS) were evaluated both offline and under closed-loop real-time control, with online data further used to refine the models. Main results. DPARS achieved real-time performance comparable to CNN ( R2 = 0.750 vs 0.777), while reducing model size by 7 × (227.8 KB) and forward pass latency by 120 × (0.25 ms), resulting in approximately 6 × lower energy usage (0.0208 Wh) in embedded execution. Significance. These findings highlight the importance of integrated system design for multi-degrees of freedom EMG decoding, where performance emerges from the interplay between preprocessing, model design, and real-time constraints, enabling efficient embedded control and more intuitive, dexterous neuroprosthetics.