Identification and Decoding of EMG Signal Using SVM Classifier

The electromyography (EMG) signal is a bioelectrical signal, generated in muscles during voluntary or involuntary muscle activities. The muscle activities such as contraction or relaxation are always controlled by the nervous system. A fundamental component of many modern prostheses is the myoelectric control system, which uses the Electromyogram (EMG) signals from an individual's muscles to control the prosthesis movements.The main contribution of this paper is an investigation into accurately discriminating between individual and combined fingers movements using surface EMG signals, so that different finger postures of a prosthetic hand can be controlled in response. For this purpose, two EMG electrodes located on the human forearm are utilized to collect the EMG datafrom ten participants. Various feature sets are extracted by the sum of absolute value for each window and projected in a manner that ensures maximum separation between the finger movements and then fed to SVM classifiers. In this work 71.11% and 62.22% accuracy are achieved for five and ten classes of finger movements respectively.