EMG signal based finger movement recognition for prosthetic hand control

Electromyography (EMG) signal can be defined as a measure of electrical activity produced by skeletal muscles. It can be used in handling electronic devices or prosthesis. If we are able recognize the hand gesture captured using EMG signal with greater reliability and classification rate, it could serve a good purpose for handling the prosthesis and to provide the good quality of life to amputees and disabled people. In this paper, we have worked on recognizing the 9 classes of individual and combined finger movement captured using 2 channel EMG sensor. We have used two different classification techniques such as Artificial Neural Network (ANN), and k- nearest neighbors (KNN), to classify the test samples. Seven time domain features a) Mean absolute value, b) root mean square, c) variance, d) waveform length, e) number of zero crossing, f) complexity, g) mobility have been used to uniquely represent the EMG channel data. Tuning parameters like number of hidden layers, learning constant and number of neighbors have been determined from the experimental results to achieve the better classification results. Classification accuracy has been selected as a metric to evaluate the performance of each classifier.