An Ensemble Classifier for Finger Movement Recognition using EMG Signals

Electromyography (EMG) signals that obtained by electrodes connected to the forearm are the monitoring of the muscles by the electrical method. These signals are quite useful during the use of prosthesis as a source signal to the moving prosthesis. Therefore, it is essential that classifying the EMG signals with high accuracy by analyzing. This study aims that classifying the individual and combined finger movements using surface EMG signals taken from the surface of the human forearm. EMG signals that belong to 10 different finger movements obtained from eight subjects were used. Firstly, EMG signals have been split into segments by the windowing process, and temporal feature vectors are formed by applying various feature extraction methods to these segments.  Feature vectors have been classified with the ensemble bagged tree algorithm, which is a combination of classifiers, to obtain the correct classification decision. As a result of 10-fold cross-validation, with the proposed method, 96.6% overall classification accuracy was achieved. The results obtained show that the ensemble classifier can be used successfully in determining finger movements when compared with similar studies.