sEMG based intention identification of human body movement research on assistive robot

Abstract

Recently, the robot technology research is changing from manufacturing industry to non-manufacturing industry, especially the service industry related to the human life. Assistive robot is a kind of novel service robot. It can not only help the elder and disabled people to rehabilitate their impaired musculoskeletal functions, but also help healthy people to perform tasks requiring large forces. This kind of robot has a broad application prospect in many areas, such as medical rehabilitation, special military operations, special/high intensity physical labour, space, sports, and entertainment. Assistive robots are required to autonomously identify human’s movement intention in order to provide adequate support. Research carried out focus on using muscle surface electro as a clue for intention identification [1–2]. However an advanced real-time operating system is needed for integrating the existing approaches with assistive robot control. Moreover, for autonomous intention identification, a formulation of neural network will need to be developed for establishing connections between surface electro signals and human movement intentions. In this paper, a neural network based human movement intention identification method is proposed. sEMG (Surface Electromyography) signal of muscles will be processed through a novel wavelet decomposition method and then be used as input signals to a neural network which identifies human intentions. Based on human intentions, adequate support will be given to the human elbow by a real-time QNX control system. Firstly, sEMG of Palmaris longus, brachioradialis, flexor carpiulnaris and biceps brachii are analysed with a wavelet transform method. Then, the absolute variance of 3-layer wavelet coefficients is distilled, and regarded as signal characteristics to compose eigenvectors. The eigenvectors are input data of a neural network classifier used to identify 5 different kinds of movement patterns including wrist flexor, wrist extensor, elbow flexion, forearm pronation, and forearm rotation. Finally, a human arm elbow movement intention control experiment study is carried out in the QNX control system which has been established. The assistive robot can move according to the intention of the operator’s action and achieve good effect. The control algorithm is straightforward and real-time, using the QNX operating system. Experimental results verify the effectiveness and advancement of the proposed algorithm.