Design and implementation of an intelligent interface for myoelectric controlled prosthesis

A discrimination system, using a neural network for an electromyographic (EMG) externally controlled upper extremity prostheses is proposed. In this system, an artificial neural network (ANN) is used to learn the relation between the power spectrum of an EMG signal analyzed by fast Fourier transform (FFT) and the performance desired by handicapped people. The neural network can discriminate 7 performances of the EMG signals simultaneously. In order to prove the effectiveness of this system, experiments for discriminating the 7 arm performances of a healthy 23 year-old man, were carried out. For real-time operation, a digital signal processor (ADSP-21061) operates over the resulting set of weights and maps the incoming signal to the stimuli control domain. Results show a highly accurate discrimination of the control signal over interference patterns.