Abstract
This paper proposes a control method for a human-assisting manipulator using acceleration sensors, which consists of an arm control part and a hand and wrist control part. The arm control part controls manipulator's shoulder and elbow joints using acceleration signals, while the hand and wrist control part controls the corresponding joints using mechanomyogram (MMG) signals measured from a human operator. A distinctive feature of our method is to estimate force and motion information from the measured acceleration signals using MMG signal processing and the probabilistic neural network. It is shown from experiments that the MMG patterns during hand and wrist motions can be classified sufficiently and that the prosthetic manipulator can be controlled using the measured acceleration signals. It may be useful as an assistive device for the physically disabled.
