Surface Electromyography (sEMG)-based Intention Recognition and Control Design for Human–Robot Interaction in Uncertain Environment

Abstract

An important direction of human-robot interaction (HRI) is making robots respond to complex and dexterous tasks intelligently. To achieve this, biological signals based on surface electromyography (sEMG) have widely been used to identify human intentions rapidly and effectively. We propose an algorithm that can recognize human intentions conveyed by different hand gestures through analyzing sEMG data. This will facilitate the selection of the most appropriate interaction mode and level during HRI for the robot. We also propose an admittance control framework combining a tan-type barrier Lyapunov function (BLF) and a radial basis function neural network (RBFNN) to ensure the interaction and tracking performance and to guarantee the stability of the system in uncertain environments. Experiments performed on a Baxter robot verify the effectiveness of the proposed framework.

Research on sEMG Feature Generation and Classification Performance Based on EBGAN

“…(3) Compared with other types of GANs, this method achieves a maximum mean discrepancy that is smaller than that of the original data; (4) The experimental results of different typical classification models show that the proposed data enhancement method can effectively improve the classification accuracy of typical classification models, and the accuracy rate is improved by 1~5%.…”

“…Generally speaking, the signal source of the human-robot interaction includes detection force, position or physiological signal (EEG, EMG) [3]. Among them, because the generation of surface EMG signals is ahead of limb movements, it has the advantage of being non−invasive and has become one of the most ideal control signal sources in human-robot interaction systems [4].…”