The Development of an Underwater sEMG Signal Recognition System Based on Conductive Silicon Stroke patients need rehabilitation to recover their abilities of moving, and the underwater rehabilitation can reduce the possibility of secondary injury during the rehabilitation process. Collecting surface electromyography (sEMG) signals underwater can provide better rehabilitation guidance to the medical doctors. However, the current sEMG electrodes cannot be applied to collect sEMG signals underwater. To solve this problem, we propose a soft sEMG electrode based on conductive silicon. The time domain and frequency domain features of sEMG signals are extracted. The sEMG signals are identified by Back Propagation Neural Network (BPNN) under the dry, simulated sweating and water environments respectively. Under the dry environment, there is no significant difference in the recognition accuracy of sEMG signals between the conductive silicon electrode and the AgAgCl electrode. Under the water environment, the recognition accuracy of sEMG signals acquired by the conductive silicon electrode is 95.47% by employing time domain features.

Recognition of Finger Motions Based on Surface Electromyographic Signals and Artificial Neural Network Surface electromyographic (sEMG) signals can be used in medicine, rehabilitation, robotics and industrial fields. This paper proposes a recognition method of finger motions for dexterous control of prosthetic hands. In this work, we record the corresponding sEMG signals from five subjects, and then set up the model by data preprocessing, feature extraction and classification. The results show that high recognition accuracy can be achieved by using time domain feature extraction and artificial neural network. It means that finger motions can be decoded accurately. To acquire the tradeoff between the number of channels and the recognition accuracy, we apply channel number reduction and it is found that the number of channels is at least 7 with recognition accuracy of 90.52%.