An EMG controlled human supporting robot using neural network

Abstract

Discusses an EMG based control method of a robotic manipulator as an adaptive human supporting system, which consists of an arm control part, a hand and wrist control part and a graphical feedback display. The arm control part controls joint angles of the arm according to the position of the operator's wrist joint measured by a 3D position sensor. The hand and wrist control part selects an active joint out of four joint degrees of freedom and controls if using an impedance model based on the EMG signals. A distinctive feature of our method is to use a statistical neural network for EMG pattern discrimination. This network can adapt to changes of the EMG patterns according to differences among individuals different locations of the electrodes, time variation caused by fatigue or sweat, and so on. It is shown from the experiments that the hand and wrist motions can be controlled based on the EMG signals sufficiently. It may be useful as an assistive device for a handicapped person.