Abstract: The objective is to control manipulator off-line by using electromyography signals. Electromyography signals are collected from the biceps and triceps muscles of normal subjects when they move their elbow flexion-extension with time-varying loads. The raw electromyography signals are processed and the new defined characteristic is picked up. A four-layer feed-forward neural network model with the characteristic as its input is developed. The weighted values of the model are optimized with the adjusted back-propagation algorithm. By training the model the transformation can be mapped: From the processed eletromyography signals to the elbow joint angles. The predicted angles are used to control the manipulator by inverse-control method. The angles of the manipulator are compared with those of the elbow joint. The experimental results show that the root mean square error between the joint angle of the manipulator and the actual joint angle measured by the goniometer is less than 1°.

Key words: Electromyography signals, Manipulator, Neural network, State identification