A Wearable Master–Slave Rehabilitation Robot Based on an Epidermal Array Electrode Sleeve and Multichannel Electromyography Network

Abstract

Investigating electromyography (EMG) signals is vital to promote the development of both rehabilitative robots and understanding of the movement neural mechanism. Interactions between various muscle units are paramount to be measured through network analysis, aiming to reveal how information is propagated and integrated. Herein, an EMG network using an epidermal array electrode sleeve to record multichannel EMGs is constructed. Then, a master–slave rehabilitation robot by adopting the EMG network as a feature for movement intention recognition is built. The results demonstrate that the sleeve can record signals with high quality, characterized by better signal robustness and higher movement recognition performance. The different finger movements evoke the specific spatial network patterns, characterized by the dominated hub at the muscle in charge of the corresponding movement, and the proposed EMG network‐based approach consistently achieves the highest recognition accuracy. Moreover, the proposed approach also shows the relatively less influence of signal length and electrode positions on the movement recognition. Finally, the proposed robot system can achieve 98.21% ± 2.37 accuracy for online control. These results provide a novel theoretical and practical basis for neural prosthesis control and hemiplegic hand rehabilitation.