A human supporting manipulator using neural network and its clinical application for forearm amputation

This paper proposes a training system based on EMG signals for prosthetic control and the development of its prototype. This system aims to enhance three kinds of control ability: muscular contraction, cooperation among several muscles, and the timing of EMG generation. For EMG signal processing a statistical neural network is used which can adapt itself to changing EMG patterns according to the differences among individuals, the different locations of the electrodes, the time variation caused by fatigue or sweat, and so on. During training, EMG signal information is displayed in the feedback monitor. The experiments have been conducted using the prototype system. The subject is a 51 year old man who had his forearm amputated when he was 18 years old. After training for five days, the ability to manipulate the EMG signal of the subject has been enhanced and the effectiveness of this system is shown