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

International journal of advanced computer science & applications, 2019, Vol.10 (6)

2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2158-107X ;EISSN: 2156-5570 ;DOI: 10.14569/IJACSA.2019.0100612

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  • Title:
    Classification of Hand Movements based on Discrete Wavelet Transform and Enhanced Feature Extraction
  • Author: Too, Jingwei ; Rahim, Abdul ; Mohd, Norhashimah
  • Subjects: Classification ; Electromyography ; Machine learning ; Wavelet transforms
  • Is Part Of: International journal of advanced computer science & applications, 2019, Vol.10 (6)
  • Description: 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.
  • Publisher: West Yorkshire: Science and Information (SAI) Organization Limited
  • Language: English
  • Identifier: ISSN: 2158-107X
    EISSN: 2156-5570
    DOI: 10.14569/IJACSA.2019.0100612
  • Source: AUTh Library subscriptions: ProQuest Central
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