Acquisition and processing real-time EMG signals for prosthesis active hand movements

Abstract

In the field of Robotics, prosthesis hand amputees are highly benefited for various active hand movements based on wrist-hand mobility. The development of an advanced human-machine interface has been an interesting research topic in the field of rehabilitation, in which biomedical signals such as electromyography (EMG) signals, plays a significant role. Sensing of EMG signals concerns with the signal capturing, conditioning, feature extraction and classification of different active hand movements for controlled human-assisting robots or prosthetic applications. This paper concerns with the acquisition and analysis of EMG signals for multiple active hand movements based on wrist-hand mobility for control of prosthesis robotic hand. To recognize the effectiveness of hand prosthesis, Anterior and Posterior forearm muscles are being considered for better exploitation of EMG signals. The Feature is extracted using statistical analysis and pattern classification is done by linear discriminant analysis (LDA) with estimated classification rate of about (80-86)%.