Hand Gesture Recognition Using Mechanomyography Signal Based on LDA Classifier

Abstract

The growing number of amputees in Iraq with multiple degrees of amputations makes it necessary to provide them with prosthetic hands with an easy to use control system that meets their aspirations. The Mechanomyography (MMG) signal has been proposed as an alternative or assisting method for hand gesture recognition. Electromyography (EMG) which is used as control signal in the commercial prosthetic hands faces many challenges such as electrical interference, non-stationery and electrode displacement. The MMG signal has been presented as a method to deal with the existing challenges of EMG. In this paper, MMG based hand gesture recognition is proposed with Pattern Recognition (PR) system. MMG signal have been collected from six healthy subjects, using accelerometers and microphones, which performed seven classes of hand movements. Classification accuracy of approximately 89% was obtained with PR method, consisting of time domain and Wavelet feature extraction and Linear Discernment Analysis (LDA) for classification. The results showed that the proposed method has a promising way for detecting and classifying hand gestures by low-cost MMG sensors which can be used for the control of prosthetic hand.