Robot Control Using Electromyography (EMG) Signals of the Wrist

Abstract

The aim of this paper is to design a human–interface system, using EMG signals elicited by various wrist movements, to control a robot. EMG signals are normalized and based on joint torque. A three‐layer neural network is used to estimate posture of the wrist and forearm from EMG signals. After training the neural network and obtaining appropriate weights, the subject was able to control the robot in real time using wrist and forearm movements.