Neuro-fuzzy surface EMG pattern recognition for multifunctional hand prosthesis control

Electromyogram (EM) signal is an electrical manifestation of muscle contractions. EMG signal collected from surface of the skin, a non-invasive bioelectric signal, can be used in different rehabilitation applications and artificial extremities control. This study has proposed to utilize the surface EMG (SEMG) signal to recognize patterns of hand prosthesis movements. It suggests using an adaptive neuro-fuzzy inference system (ANFIS) to identify motion commands for the control of a prosthetic hand. In this work a hybrid method for training fuzzy system, consisting of back-propagation (BP) and least mean square (LMS) is utilized. Also in order to optimize the number of fuzzy rules, a subtractive clustering algorithm has been developed. The myoelectric signals utilized to classify, were six hand movements. Features chosen for SEMG signal were time and time-frequency domain. Neuro-fuzzy systems designed and utilized in this study were tested independently and in a combined manner for both time and time-frequency features. The results showed that the combined feature implementation was the best in regard to identification of required movement tasks. The average accuracy of system for the combined approach was 96%.