Action recognition and control of mechanical simulated arm: electromyographic signal detection

Abstract

Electromyography (EMG) signal contains a large amount of human motion information, which can be used to classify human actions. In this study, based on the detection of surface electromyography (sEMG) signal, three actions were designed, the sEMG signal was collected by the EMG acquisition system. Four feature values, including root-mean-square value, average absolute value (MAV), wavelength, and Zero crossing point, were extracted from the signal. Then these values were taken as the input of Back-Propagation neural network (BPNN) to recognize different actions, thereby realizing the real-time control of mechanical simulated arm. The experiment found that the training time of the BPNN method designed in this study was short, 11.36 s, and the average recognition accuracy rate reached 92.2%. In the real-time control experiment of mechanical simulated arm, the recognition accuracy of different actions reached more than 90%, and the running time was short. The experimental results verifies the effectiveness of the proposed method and make some contributions to the efficient control of the mechanical simulation arm.