Human Hand Movement Classification based on EMG Signal using different Feature Extractor

Abstract

Electromyography (EMG) based hand movement classification plays a significant role in various fields, namely in prosthetics, rehabilitation, biomechanics, etc. This paper presents the study of EMG-based hand movement classification of 3 human hand gestures (hand at rest, wrist flexion, and wrist extension). The dataset was officially collected from the University of California, Irvine (UCI) machine learning repository. The dataset contains 8 channels and 3 classes representing 3 human hand gestures, with 15000 rows of EMG data for each class. The dataset obtained was raw and unprocessed, to filter this dataset Notch and Butterworth filters were used. After filtering, the sliding window was performed. Various feature extraction techniques, namely frequency domain features (FD) and discrete wavelet transform (DWT) were applied separately on the window dataset and then accuracy was tested on different classifiers, namely random forest (RF), k- nearest neighbor (KNN), and decision tree (DT). As a novel approach, time domain (TD) and DWT extracted features were fused together and then given to the classifiers to test accuracy. Among all these feature extractors, the features extracted by FD provided the highest accuracy of 81.69 for the RF classifier.