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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Yukiya NAKAI, Makoto SASAKI, Katsuhiro KAMATA, Atsushi NAKAYAMA
Formato:	article
Lenguaje:	EN
Publicado:	The Japan Society of Mechanical Engineers 2019
Materias:	surface electromyography-based interface suprahyoid muscles oral motion classification electric wheelchair support vector machine Mechanical engineering and machinery TJ1-1570
Acceso en línea:	https://doaj.org/article/c0dce470acf14d06917fb1d539f06331
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:c0dce470acf14d06917fb1d539f06331
record_format	dspace
spelling	oai:doaj.org-article:c0dce470acf14d06917fb1d539f063312021-11-29T05:48:33ZDevelopment of sEMG-based robust oral motion classification method and its application to electric wheelchair operation2187-974510.1299/mej.19-00144https://doaj.org/article/c0dce470acf14d06917fb1d539f063312019-11-01T00:00:00Zhttps://www.jstage.jst.go.jp/article/mej/6/6/6_19-00144/_pdf/-char/enhttps://doaj.org/toc/2187-9745Interfaces 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.Yukiya NAKAIMakoto SASAKIKatsuhiro KAMATAAtsushi NAKAYAMAThe Japan Society of Mechanical Engineersarticlesurface electromyography-based interfacesuprahyoid musclesoral motion classificationelectric wheelchairsupport vector machineMechanical engineering and machineryTJ1-1570ENMechanical Engineering Journal, Vol 6, Iss 6, Pp 19-00144-19-00144 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	surface electromyography-based interface suprahyoid muscles oral motion classification electric wheelchair support vector machine Mechanical engineering and machinery TJ1-1570
spellingShingle	surface electromyography-based interface suprahyoid muscles oral motion classification electric wheelchair support vector machine Mechanical engineering and machinery TJ1-1570 Yukiya NAKAI Makoto SASAKI Katsuhiro KAMATA Atsushi NAKAYAMA Development of sEMG-based robust oral motion classification method and its application to electric wheelchair operation
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.
format	article
author	Yukiya NAKAI Makoto SASAKI Katsuhiro KAMATA Atsushi NAKAYAMA
author_facet	Yukiya NAKAI Makoto SASAKI Katsuhiro KAMATA Atsushi NAKAYAMA
author_sort	Yukiya NAKAI
title	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_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_sort	development of semg-based robust oral motion classification method and its application to electric wheelchair operation
publisher	The Japan Society of Mechanical Engineers
publishDate	2019
url	https://doaj.org/article/c0dce470acf14d06917fb1d539f06331
work_keys_str_mv	AT yukiyanakai developmentofsemgbasedrobustoralmotionclassificationmethodanditsapplicationtoelectricwheelchairoperation AT makotosasaki developmentofsemgbasedrobustoralmotionclassificationmethodanditsapplicationtoelectricwheelchairoperation AT katsuhirokamata developmentofsemgbasedrobustoralmotionclassificationmethodanditsapplicationtoelectricwheelchairoperation AT atsushinakayama developmentofsemgbasedrobustoralmotionclassificationmethodanditsapplicationtoelectricwheelchairoperation
_version_	1718407610897530880

Development of sEMG-based robust oral motion classification method and its application to electric wheelchair operation

Ejemplares similares