Classification Of Hand Movements Based On Discrete Wavelet Transform And Enhanced Feature Extraction

Too, Jing Wei and Abdullah, Abdul Rahim and Mohd Saad, Norhashimah (2019) Classification Of Hand Movements Based On Discrete Wavelet Transform And Enhanced Feature Extraction. International Journal Of Advanced Computer Science And Applications (IJASCA), 10 (6). pp. 83-89. ISSN 2158-107X

Text CLASSIFICATION OF HAND MOVEMENTS BASED ON DISCRETE WAVELET TRANSFORM AND ENHANCED FEATURE EXTRACTION.PDF Download (579kB)

Abstract

Extraction of potential electromyography (EMG) features has become one of the important roles in EMG pattern recognition. In this paper, two EMG features, namely, enhanced wavelength (EWL) and enhanced mean absolute value (EMAV) are proposed. The EWL and EMAV are the modified version of wavelength (WL) and mean absolute value (MAV), which aims to enhance the prediction accuracy for the classification of hand movements. Initially, the proposed features are extracted from the EMG signals via discrete wavelet transform (DWT). The extracted features are then fed into the machine learning algorithm for classification process. Four popular machine learning algorithms include k-nearest neighbor (KNN), linear discriminate analysis (LDA), Naïve Bayes (NB) and support vector machine (SVM) are used in evaluation. To examine the effectiveness of EWL and EMAV, several conventional EMG features are used in performance comparison. In addition, the efficacy of EWL and EMAV when combine with other features are also investigated. Based on the results obtained, the combination of EWL and EMAV with other features can improve the classification performance. Thus, EWL and EMAV can be considered as valuable tools for rehabilitation and clinical applications

Item Type:	Article
Uncontrolled Keywords:	Electromyography, Feature Extraction, Discrete Wavelet Transform, Classification, Pattern Recognition
Divisions:	Faculty of Electrical Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	09 Dec 2020 11:53
Last Modified:	09 Dec 2020 11:53
URI:	http://eprints.utem.edu.my/id/eprint/24599
Statistic Details:	View Download Statistic

Actions (login required)

View Item