Prosthetic hand control based on torque estimation from EMG signals

In this paper, we propose a direct torque control method for the prosthetic hand. In order to estimate the joint torque from EMG signals, an artificial neural network by the feedback error learning schema is used. 2-DOF motions, i.e. hand grasping/opening and arm flexion/extension, are picked up. In the experiments, two measurement conditions of EMG signal are prepared: the forearm from which the EMG signal is measured is free or fixed. Then it is verified that the neural network can learn the relation between the EMG signal and the joint torque under these two measurement conditions.