Acquisition of EMG signals to recognize multiple Hand Gestures for Prosthesis Robotic Hand-A Review
Pages : 65-70
Robotic prosthesis hand amputees are highly benefited, which would allow the various hand gestures based on wrist and fingers movements. In the field of Biomedical signal processing, development of an advanced human–machine interface has been an interesting research topic in the field of rehabilitation, in which electromyography (EMG) signals, have a vital role to play. EMG signal is an electrical activity of Muscles and usually represented as a function of time, defined in terms of amplitude, frequency and phase. EMG signal based reliable and efficient hand gesture recognition can help to develop good human computer interface which in turn will increase the quality of life of the disabled or aged people. Acquisition and analysis of EMG signals concerns with the detection, processing, feature extraction, classification and application for control human-assisting Robots or prosthetic applications. This paper reviews recent research and development of hand prosthetic for multiple hand gestures based on wrist-hand mobility subsequent from the EMG signals. To identify the effectiveness of hand prosthesis, forearm muscles are being considered for better exploitation of EMG signals and classification of movements is done by Wavelet transform followed by efficient time-frequency featuring in Artificial Neural Networks (ANN).
Keywords: Artificial Neural Networks, Classification, EMG signal, Feature extraction, Wavelet transform.
Article published in International Journal of Current Engineering and Technology, Vol.4,No.1 (Feb- 2014)