EMG Signal Classification and Feature Extraction using Machine Learning and DWT Technique

Abstract

In this paper, a classifier has been designed using Support Vector Machine (SVM) to classify Electromyography (EMG) signals. Given the EMG signals, the SVM-based classifier aims to classify ten individual and combined fingers motion command into one of the predefined set of movements. Prior to classification, EMG data is segmented with a DWT such as Mean Absolute Value (MAV), Root Mean Square (RMS) and SD are extracted for each window and combined to a feature set. Extracted features are used as inputs to the classification system. A linear SVM (one-against-one method) is used for the multiclass classification of EMG signals. DWT sizes that affect the classification performance have been reported. The best feature set that ensures maximum discrimination between the finger movements has also been reported. Validation shows that support vector machine can classify EMG signals correctly with a higher classification accuracy at 91.7% suitable for designing for proposed method.