抄録
This paper discusses an EMG based control method of a robotic manipulator as an adaptive human supporting system, which consists of an arm control part and a hand and wrist control part. The arm control part controls joint angles of the manipulator's 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 it according to EMG signals measured from a human operator. A distinctive feature of our method is to use a statistical neural network for EMG pattern classification. This network can acquire stochastic representation of measured EMG patterns through learning based on the log-linearized Gaussian mixture model, so that it can adapt changes of the EMG patterns according to the 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 EMG patterns during hand and wrist motions can be classified sufficiently by using the network. It may be useful as an assistive device for handicapped persons.
