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Feature Extraction Evaluation of Various Machine Learning Methods for Finger Movement Classification using Double Myo Armband

Journal of Engineering and Technological Sciences · Vol. 55 No. 5 (2023) · hlm. 587–599

DOI
10.5614/j.eng.technol.sci.2023.55.5.8
ISSN
2338-5502, 2337-5779
Terbit
2023-12-29
Penerbit
Directorate for Research and Community Services, Institut Teknologi Bandung
Bahasa
eng

Abstrak

The deployment of electromyography (EMG) signals attracts many researchers since it can be used in decoding finger movements for exoskeleton robotics, prosthetics hand, and powered wheelchair. However, decoding any movement is a challenging task. The success of EMG signals' use lies in the appropriate choice of feature extraction and classification model, especially in the feature extraction process. Therefore, this study evaluates an eight-feature extraction evaluation on various machine learnings such as the Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree (DT), Naïve Bayes (NB), and Quadratic Discriminant Analysis (QDA). The dataset from four intact subjects is used to classify twelve finger movements. Through 5 cross-validations, the result shows that almost all feature extractions combined with SVM outperform other combinations of features and classifiers. Mean Absolute Value (MAV) as a feature and SVM as a classifier highlight the best combination with an accuracy of 94.01%.

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  • Universitas Jember
  • National Taiwan University of Science and Technology
  • University of Technology Sydney
  • Friedrich-Alexander-Universität Erlangen-Nürnberg
  • Universitas Gadjah Mada
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  • Organisasi sama
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Rekan penulisOrganisasiArtikel bersama
Sumardi SumardiUniversitas Gadjah Mada2
Dheny Dwi ArdhiansyahUniversitas Jember2
Arizal Mujibtamala Nanda ImronUniversitas Jember2
Naufal Ainur RizalUniversitas Jember2
Mochamad Edoward RamadhanUniversitas Jember2
Aris Zainul MuttaqinUniversitas Jember2
Claudio CastelliniFriedrich-Alexander-Universität Erlangen-Nürnberg2
Adel Ali Al-JumailyUniversity of Technology Sydney1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journals.itb.ac.id/index.php/jets/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Feature Extraction Evaluation of Various Machine Learning Methods for Finger Movement Classification using Double Myo Armband</dc:title>
	<dc:creator>Anam, Khairul</dc:creator>
	<dc:creator>Ismail, Harun</dc:creator>
	<dc:creator>Hanggara, Faruq Sandi</dc:creator>
	<dc:creator>Avian, Cries</dc:creator>
	<dc:creator>Nahela, Safri</dc:creator>
	<dc:creator>Sasono, Muchamad Arif Hana</dc:creator>
	<dc:subject xml:lang="en-US">classification</dc:subject>
	<dc:subject xml:lang="en-US">electromyography</dc:subject>
	<dc:subject xml:lang="en-US">feature extraction</dc:subject>
	<dc:subject xml:lang="en-US">finger movement</dc:subject>
	<dc:subject xml:lang="en-US">machine learning</dc:subject>
	<dc:description xml:lang="en-US">The deployment of electromyography (EMG) signals attracts many researchers since it can be used in decoding finger movements for exoskeleton robotics, prosthetics hand, and powered wheelchair. However, decoding any movement is a challenging task. The success of EMG signals' use lies in the appropriate choice of feature extraction and classification model, especially in the feature extraction process. Therefore, this study evaluates an eight-feature extraction evaluation on various machine learnings such as the Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree (DT), Naïve Bayes (NB), and Quadratic Discriminant Analysis (QDA). The dataset from four intact subjects is used to classify twelve finger movements. Through 5 cross-validations, the result shows that almost all feature extractions combined with SVM outperform other combinations of features and classifiers. Mean Absolute Value (MAV) as a feature and SVM as a classifier highlight the best combination with an accuracy of 94.01%.</dc:description>
	<dc:publisher xml:lang="en-US">Directorate for Research and Community Services, Institut Teknologi Bandung</dc:publisher>
	<dc:date>2023-12-29</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jets/article/view/20425</dc:identifier>
	<dc:identifier>10.5614/j.eng.technol.sci.2023.55.5.8</dc:identifier>
	<dc:source xml:lang="en-US">Journal of Engineering and Technological Sciences; Vol. 55 No. 5 (2023); 587-599</dc:source>
	<dc:source>2338-5502</dc:source>
	<dc:source>2337-5779</dc:source>
	<dc:source>10.5614/j.eng.technol.sci.2023.55.5</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jets/article/view/20425/6446</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Journal of Engineering and Technological Sciences</dc:rights>
</oai_dc:dc>