Myoelectric Signal Classification of Targeted Muscles Using Dictionary Learning
Surface electromyography (sEMG) signals comprise electrophysiological information related to muscle activity. As this signal is easy to record, it is utilized to control several myoelectric prostheses devices. Several studies have been conducted to process sEMG signals more efficiently. However, res...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/10/2370 |
| _version_ | 1818035229171908608 |
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| author | Hyun-Joon Yoo Hyeong-jun Park Boreom Lee |
| author_facet | Hyun-Joon Yoo Hyeong-jun Park Boreom Lee |
| author_sort | Hyun-Joon Yoo |
| collection | DOAJ |
| description | Surface electromyography (sEMG) signals comprise electrophysiological information related to muscle activity. As this signal is easy to record, it is utilized to control several myoelectric prostheses devices. Several studies have been conducted to process sEMG signals more efficiently. However, research on optimal algorithms and electrode placements for the processing of sEMG signals is still inconclusive. In addition, very few studies have focused on minimizing the number of electrodes. In this study, we investigated the most effective method for myoelectric signal classification with a small number of electrodes. A total of 23 subjects participated in the study, and the sEMG data of 14 different hand movements of the subjects were acquired from targeted muscles and untargeted muscles. Furthermore, the study compared the classification accuracy of the sEMG data using discriminative feature-oriented dictionary learning (DFDL) and other conventional classifiers. DFDL demonstrated the highest classification accuracy among the classifiers, and its higher quality performance became more apparent as the number of channels decreased. The targeted method was superior to the untargeted method, particularly when classifying sEMG signals with DFDL. Therefore, it was concluded that the combination of the targeted method and the DFDL algorithm could classify myoelectric signals more effectively with a minimal number of channels. |
| first_indexed | 2024-12-10T06:51:44Z |
| format | Article |
| id | doaj.art-8b8bf642a3654af3999eb29004bce527 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-12-10T06:51:44Z |
| publishDate | 2019-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-8b8bf642a3654af3999eb29004bce5272022-12-22T01:58:33ZengMDPI AGSensors1424-82202019-05-011910237010.3390/s19102370s19102370Myoelectric Signal Classification of Targeted Muscles Using Dictionary LearningHyun-Joon Yoo0Hyeong-jun Park1Boreom Lee2Department of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Gwangju 61005, KoreaDepartment of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Gwangju 61005, KoreaDepartment of Biomedical Science and Engineering (BMSE), Institute of Integrated Technology (IIT), Gwangju Institute of Science and Technology (GIST), Gwangju 61005, KoreaSurface electromyography (sEMG) signals comprise electrophysiological information related to muscle activity. As this signal is easy to record, it is utilized to control several myoelectric prostheses devices. Several studies have been conducted to process sEMG signals more efficiently. However, research on optimal algorithms and electrode placements for the processing of sEMG signals is still inconclusive. In addition, very few studies have focused on minimizing the number of electrodes. In this study, we investigated the most effective method for myoelectric signal classification with a small number of electrodes. A total of 23 subjects participated in the study, and the sEMG data of 14 different hand movements of the subjects were acquired from targeted muscles and untargeted muscles. Furthermore, the study compared the classification accuracy of the sEMG data using discriminative feature-oriented dictionary learning (DFDL) and other conventional classifiers. DFDL demonstrated the highest classification accuracy among the classifiers, and its higher quality performance became more apparent as the number of channels decreased. The targeted method was superior to the untargeted method, particularly when classifying sEMG signals with DFDL. Therefore, it was concluded that the combination of the targeted method and the DFDL algorithm could classify myoelectric signals more effectively with a minimal number of channels.https://www.mdpi.com/1424-8220/19/10/2370electrodeselectromyographyprosthetic handmyoelectric controldictionary learning |
| spellingShingle | Hyun-Joon Yoo Hyeong-jun Park Boreom Lee Myoelectric Signal Classification of Targeted Muscles Using Dictionary Learning Sensors electrodes electromyography prosthetic hand myoelectric control dictionary learning |
| title | Myoelectric Signal Classification of Targeted Muscles Using Dictionary Learning |
| title_full | Myoelectric Signal Classification of Targeted Muscles Using Dictionary Learning |
| title_fullStr | Myoelectric Signal Classification of Targeted Muscles Using Dictionary Learning |
| title_full_unstemmed | Myoelectric Signal Classification of Targeted Muscles Using Dictionary Learning |
| title_short | Myoelectric Signal Classification of Targeted Muscles Using Dictionary Learning |
| title_sort | myoelectric signal classification of targeted muscles using dictionary learning |
| topic | electrodes electromyography prosthetic hand myoelectric control dictionary learning |
| url | https://www.mdpi.com/1424-8220/19/10/2370 |
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