Estimation of forearm movement from EMG signal and application to prosthetic hand control

In this paper, a new approach is examined for controlling the prosthetic hand: the torque control of each joint. The joint torque is estimated from EMG signals using an artificial neural network. The learning system is based on a feedback error learning schema. Two controlled objects were used in the experiments, i.e. a hand dynamic model and prosthetic hand. These experimental results show the possibility to apply the proposed method to the prosthetic hand control under the conditions that the controlled object is a second order delay system.