On-line learning method for EMG prosthetic hand control

This paper proposes a novel learning method for prosthetic hand control. Conventional works have used off-line learning methods for control, and hence two kinds of training must be carried out separately: one is for the amputee to control prosthetic hands, and the other is for the prosthetic hand controller to adapt to the amputee's variations. Consequently, an amputee cannot acquire the sensations of operating prosthetic hands through training, and nevertheless he or she is likely to experience difficulties in forcing the prosthetic hand controller to follow the change of his or her own characteristics in practical use. We accordingly design an on-line learning mechanism which can adapt to the individual's characteristics in real time. Using this mechanism, a suitable mapping function of the surface electromyogram (EMG) to motions of prosthetic hands can be acquired according to the amputee's evaluation in practical use. Thereby, the mechanism realizes a shortening of training time and adaptation to individual variation in real time. The experiments succeeded in discriminating six forearm motions to verify the proposed method. First, we use intrinsic exercise images to control a prosthetic hand, and compare our on-line method with one conventional off-line method. Second, we use EMG signals on shoulder girdles to control the prosthetic hand for upper elbow amputation. The discrimination rate in forearm EMG experiments is 89.9% by our method and 80.3% by the conventional method. Moreover, we show the possibility of applying the on-line learning method to upper elbow amputees, because a discrimination rate of 79.3% is achieved by our method in shoulder girdle EMG classification. (C) 2001 Scripta Technica.