Gesture Recognition and Master–Slave Control of a Manipulator Based on sEMG and Convolutional Neural Network–Gated Recurrent Unit

Abstract

Surface electromyography signal (sEMG) is the bioelectric signal accompanied by muscle contraction. For master–slave 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.