Human–robot interface based on sEMG envelope signal for the collaborative wearable robot

Surface electromyography (sEMG) control interface is a common method for human-centered robotics. Researchers have frequently improved the recognition accuracy of sEMG through multichannel or high-precision signal acquisition devices. However, this increases the cost and complexity of the control system. Therefore, this study developed a control interface based on the sEMG enveloped signal for a collaborative wearable robot to improve the accuracy of sEMG recognition based on the time-domain (TD) features. Specifically, an acquisition device is developed to obtain the sEMG envelope signal, and 11 types of TD features are extracted from the sEMG envelope signal acquired from the upper limb. Furthermore, a dimension reduction method based on the correlation coefficient is proposed, transforming the 11-dimensional feature into a five-dimensional envelope feature set without decreasing the accuracy. Moreover, a recognition algorithm based on a neural network has also been proposed for gesture classification. Finally, the recognition accuracy of the proposed method, principal component analysis (PCA) feature set, and Hudgins TD feature set is compared, with their accuracy at 84.39%, 72.44%, and 70.89%, respectively. Therefore, the results indicate that the envelope feature set performs better than the common gesture recognition method based on signal channel sEMG envelope signal.