Identification of real-time active hand movements EMG signals for control of prosthesis robotic hand

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. Identification, pre-processing, feature extraction and classification analysis in EMG is very desirable because it allows more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings for prosthetic applications. This paper deals with the identification of real-time active hand movements EMG signals based on wrist-hand mobility for simultaneous control of prosthesis robotic hand. The Anterior and Posterior forearm muscles are being considered for efficient exploitation of EMG signals. The Feature is extracted using statistical time-frequency scaling analysis and pattern classification is done by linear discriminant analysis (LDA) with estimated classification rate and standard deviation of about (88-91)% ± (0.1-0.3)%.