Human-emg Prosthetic Hand Interface Using Neural Network
نویسندگان
چکیده
For the improvement of the amputee’s activity of daily living (ADL), several kinds of electromyogram (EMG) controlled prosthetic hands have been developed so far. But there is still significant difference between the movements of these hands and human ones. 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. 2DOF motions, i.e. hand grasping/opening and arm flexion/extension, are picked up. Then it is verified that the neural network can learn the relation between the EMG signal and joint torque.
منابع مشابه
Design and Performance Analysis of Artificial Neural Network for Hand Motion Detection from EMG Signals
Submitted: Jan 9, 2013; Accepted: Feb 17, 2013; Published: Jul 27, 2013 Abstract: Besides prosthetic device control and neuromuscular disease identification, electromyography (EMG) signals can also be applied in the field of human computer interaction (HCI) system. This article represents the classification of Electromygraphy (EMG) signal for the detection of different predefined hand motions (...
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