Development of sEMG-based robust oral motion classification method and its application to electric wheelchair operation
Interfaces based on surface electromyography (sEMG) signals are one of the important methods for non-invasively extracting the intention of a severely disabled person and supporting environmental control of wheelchairs and personal computers. However, sEMG-based interfaces generally have a common an...
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| Format: | Article |
| Language: | English |
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The Japan Society of Mechanical Engineers
2019-11-01
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| Series: | Mechanical Engineering Journal |
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| Online Access: | https://www.jstage.jst.go.jp/article/mej/6/6/6_19-00144/_pdf/-char/en |
| _version_ | 1819194803653443584 |
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| author | Yukiya NAKAI Makoto SASAKI Katsuhiro KAMATA Atsushi NAKAYAMA |
| author_facet | Yukiya NAKAI Makoto SASAKI Katsuhiro KAMATA Atsushi NAKAYAMA |
| author_sort | Yukiya NAKAI |
| collection | DOAJ |
| description | Interfaces based on surface electromyography (sEMG) signals are one of the important methods for non-invasively extracting the intention of a severely disabled person and supporting environmental control of wheelchairs and personal computers. However, sEMG-based interfaces generally have a common and maximum disadvantage of vulnerability to changes in electrode position. In this study, we aimed to develop a robust oral motion classification method that is robust to change in electrode position. Five healthy adult male subjects participated in this experiment. sEMG signals of the suprahyoid muscles during five oral motions (right, left, up tongue motion, jaw opening, and clenching) were measured using a boomerang-shaped 22-channel electrode adhered to the underside of the jaw. Oral motion classification from sEMG signals was performed using a support vector machine (SVM). When sEMG signals measured at a position different from the 22-channnel electrode position where the training data for SVM classifier was obtained were used as the test data, the classification accuracy of five oral motions sharply decreased from 92.0% to 72.8%. In contrast, when the 10 trials of sEMG signals obtained in advance at different electrode positions on different days were used as training data, the robustness against electrode position change was improved drastically and the mean classification accuracy of all subjects reached 90.4%. Furthermore, we developed an electric wheelchair control system that can operate based on classified motions and verified its usefulness for wheelchair operability and driving performance thorough the experiment. The results showed that the proposed method can omit the SVM training process required every time after the electrode is attached and can operate the wheelchair immediately after electrode attachment. Such advancement of interfaces eliminates the annoyance caused to the user who uses the interface on a daily basis and is expected to lead to an improvement in the quality of life. |
| first_indexed | 2024-12-23T02:02:40Z |
| format | Article |
| id | doaj.art-c0dce470acf14d06917fb1d539f06331 |
| institution | Directory Open Access Journal |
| issn | 2187-9745 |
| language | English |
| last_indexed | 2024-12-23T02:02:40Z |
| publishDate | 2019-11-01 |
| publisher | The Japan Society of Mechanical Engineers |
| record_format | Article |
| series | Mechanical Engineering Journal |
| spelling | doaj.art-c0dce470acf14d06917fb1d539f063312022-12-21T18:03:58ZengThe Japan Society of Mechanical EngineersMechanical Engineering Journal2187-97452019-11-016619-0014419-0014410.1299/mej.19-00144mejDevelopment of sEMG-based robust oral motion classification method and its application to electric wheelchair operationYukiya NAKAI0Makoto SASAKI1Katsuhiro KAMATA2Atsushi NAKAYAMA3Graduate School of Engineering, Iwate UniversityGraduate School of Engineering, Iwate UniversityGraduate School of Engineering, Iwate UniversityDepartment of Engineering for Future Innovation, National Institute of Technology, Ichinoseki CollegeInterfaces based on surface electromyography (sEMG) signals are one of the important methods for non-invasively extracting the intention of a severely disabled person and supporting environmental control of wheelchairs and personal computers. However, sEMG-based interfaces generally have a common and maximum disadvantage of vulnerability to changes in electrode position. In this study, we aimed to develop a robust oral motion classification method that is robust to change in electrode position. Five healthy adult male subjects participated in this experiment. sEMG signals of the suprahyoid muscles during five oral motions (right, left, up tongue motion, jaw opening, and clenching) were measured using a boomerang-shaped 22-channel electrode adhered to the underside of the jaw. Oral motion classification from sEMG signals was performed using a support vector machine (SVM). When sEMG signals measured at a position different from the 22-channnel electrode position where the training data for SVM classifier was obtained were used as the test data, the classification accuracy of five oral motions sharply decreased from 92.0% to 72.8%. In contrast, when the 10 trials of sEMG signals obtained in advance at different electrode positions on different days were used as training data, the robustness against electrode position change was improved drastically and the mean classification accuracy of all subjects reached 90.4%. Furthermore, we developed an electric wheelchair control system that can operate based on classified motions and verified its usefulness for wheelchair operability and driving performance thorough the experiment. The results showed that the proposed method can omit the SVM training process required every time after the electrode is attached and can operate the wheelchair immediately after electrode attachment. Such advancement of interfaces eliminates the annoyance caused to the user who uses the interface on a daily basis and is expected to lead to an improvement in the quality of life.https://www.jstage.jst.go.jp/article/mej/6/6/6_19-00144/_pdf/-char/ensurface electromyography-based interfacesuprahyoid musclesoral motion classificationelectric wheelchairsupport vector machine |
| spellingShingle | Yukiya NAKAI Makoto SASAKI Katsuhiro KAMATA Atsushi NAKAYAMA Development of sEMG-based robust oral motion classification method and its application to electric wheelchair operation Mechanical Engineering Journal surface electromyography-based interface suprahyoid muscles oral motion classification electric wheelchair support vector machine |
| title | Development of sEMG-based robust oral motion classification method and its application to electric wheelchair operation |
| title_full | Development of sEMG-based robust oral motion classification method and its application to electric wheelchair operation |
| title_fullStr | Development of sEMG-based robust oral motion classification method and its application to electric wheelchair operation |
| title_full_unstemmed | Development of sEMG-based robust oral motion classification method and its application to electric wheelchair operation |
| title_short | Development of sEMG-based robust oral motion classification method and its application to electric wheelchair operation |
| title_sort | development of semg based robust oral motion classification method and its application to electric wheelchair operation |
| topic | surface electromyography-based interface suprahyoid muscles oral motion classification electric wheelchair support vector machine |
| url | https://www.jstage.jst.go.jp/article/mej/6/6/6_19-00144/_pdf/-char/en |
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