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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Classification of Hand Movements based on Discrete Wavelet Transform and Enhanced Feature Extraction

Author 1: Jingwei Too
Author 2: Abdul Rahim Abdullah
Author 3: Norhashimah Mohd Saad

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2019.0100612

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 6, 2019.

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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.

Keywords: Electromyography; feature extraction; discrete wavelet transform; classification; pattern recognition

Jingwei Too, Abdul Rahim Abdullah and Norhashimah Mohd Saad, “Classification of Hand Movements based on Discrete Wavelet Transform and Enhanced Feature Extraction” International Journal of Advanced Computer Science and Applications(IJACSA), 10(6), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100612

@article{Too2019,
title = {Classification of Hand Movements based on Discrete Wavelet Transform and Enhanced Feature Extraction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100612},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100612},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {6},
author = {Jingwei Too and Abdul Rahim Abdullah and Norhashimah Mohd Saad}
}


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