Surface electromyography signal (sEMG) is the bioelectric signal accompanied by muscle contraction. For masterslave manipulation scenario such as patients with prosthetic hands, their upper limb sEMG signals can be collected and corresponded to the patient' s gesture intention. Therefore, using a slave manipulator that integrated with the sEMG signal recognition module, the master side could control it to make gestures and meet their needs of daily life. In this paper, gesture recognition is carried out based on sEMG and deep learning, and the master-slave control of manipulator is realized. According to the results of training, the network model with the highest accuracy of gesture classification and recognition can be obtained. Then, combined with the integrated manipulator, the control signal of the manipulator corresponding to the gesture is sent to the control module of the manipulator. In the end, a prototype system is built and the master-slave control of the manipulator using the sEMG signal is realized.

Surface electromyography signal (sEMG) is the bioelectric signal accompanied by muscle contraction. In gesture recognition, sEMG is a non-invasive, efficient and fast recognition method. For patients with hand amputation, their upper limb EMG signals can be collected, and these EMG signals correspond to the patient’s hand movement intention. Therefore, by wearing the prosthetic hand integrated with the EMG signal recognition module, patients with hand amputation can also make gestures meet their needs of daily life. In this paper, gesture recognition is carried out based on sEMG and deep learning, and the master-slave control of manipulator is realized. At the same time, gesture recognition can also be applied to remote control. Controlling the end of the manipulator at a certain distance with a specific gesture can complete some tasks in complex and high-risk environments with higher efficiency. Based on Convolutional Neural Network (CNN) and Gate Recurrent Unit (GRU), this paper constructs three neural networks with different structures, including single CNN, single GRU and CNN-GRU, and then train the collected gesture data set. According to the results of test set, the input type with the highest accuracy of gesture classification and recognition can be obtained. Among the three neural networks, CNN-GRU has the highest accuracy on the test set, reaching 92%, so it is used as the selected gesture recognition network. Finally, combined with the integrated manipulator, the EMG signals collected in real time by the myo EMG signal acquisition armband are classified by the upper computer, and the results are obtained. Then the control signal of the manipulator corresponding to the gesture is sent to the Arduino control module of the manipulator, and the master-slave control of the manipulator using the EMG signal can be realized.