Human facial neural activities and gesture recognition for machine-interfacing applications

M Hamedi1, Sh-Hussain Salleh2, TS Tan2, K Ismail2, J Ali3, C Dee-Uam4, C Pavaganun4, PP Yupapin51Faculty of Biomedical and Health Science Engineering, Department of Biomedical Instrumentation and Signal Processing, University of Technology Malaysia, Skudai, 2Centre for Biomedical Engineering Transpo...

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Autores principales:	Hamedi M, Salleh SH, Tan TS, Ismail K, Ali J, Dee-Uam C, Pavaganun C, Yupapin PP
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2011
Materias:	Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/87c1960a84d243f8a96116f8b76b6122
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spelling	oai:doaj.org-article:87c1960a84d243f8a96116f8b76b61222021-12-02T07:53:26ZHuman facial neural activities and gesture recognition for machine-interfacing applications1176-91141178-2013https://doaj.org/article/87c1960a84d243f8a96116f8b76b61222011-12-01T00:00:00Zhttp://www.dovepress.com/human-facial-neural-activities-and-gesture-recognition-for-machine-int-a8895https://doaj.org/toc/1176-9114https://doaj.org/toc/1178-2013M Hamedi1, Sh-Hussain Salleh2, TS Tan2, K Ismail2, J Ali3, C Dee-Uam4, C Pavaganun4, PP Yupapin51Faculty of Biomedical and Health Science Engineering, Department of Biomedical Instrumentation and Signal Processing, University of Technology Malaysia, Skudai, 2Centre for Biomedical Engineering Transportation Research Alliance, 3Institute of Advanced Photonics Science, Nanotechnology Research Alliance, University of Technology Malaysia (UTM), Johor Bahru, Malaysia; 4College of Innovative Management, Valaya Alongkorn Rajabhat University, Pathum Thani, 5Nanoscale Science and Engineering Research Alliance (N&#39;SERA), Advanced Research Center for Photonics, Faculty of Science, King Mongkut&#39;s Institute of Technology Ladkrabang, Bangkok, ThailandAbstract: The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human&ndash;machine interface (HMI) technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG)-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2&ndash;11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy &gt;90% of chosen combinations proved their ability to be used as command controllers.Keywords: neural system, neural activity, electromyography, machine learning, muscle activityHamedi MSalleh SHTan TSIsmail KAli JDee-Uam CPavaganun CYupapin PPDove Medical PressarticleMedicine (General)R5-920ENInternational Journal of Nanomedicine, Vol 2011, Iss default, Pp 3461-3472 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine (General) R5-920
spellingShingle	Medicine (General) R5-920 Hamedi M Salleh SH Tan TS Ismail K Ali J Dee-Uam C Pavaganun C Yupapin PP Human facial neural activities and gesture recognition for machine-interfacing applications
description	M Hamedi1, Sh-Hussain Salleh2, TS Tan2, K Ismail2, J Ali3, C Dee-Uam4, C Pavaganun4, PP Yupapin51Faculty of Biomedical and Health Science Engineering, Department of Biomedical Instrumentation and Signal Processing, University of Technology Malaysia, Skudai, 2Centre for Biomedical Engineering Transportation Research Alliance, 3Institute of Advanced Photonics Science, Nanotechnology Research Alliance, University of Technology Malaysia (UTM), Johor Bahru, Malaysia; 4College of Innovative Management, Valaya Alongkorn Rajabhat University, Pathum Thani, 5Nanoscale Science and Engineering Research Alliance (N&#39;SERA), Advanced Research Center for Photonics, Faculty of Science, King Mongkut&#39;s Institute of Technology Ladkrabang, Bangkok, ThailandAbstract: The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human&ndash;machine interface (HMI) technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG)-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2&ndash;11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy &gt;90% of chosen combinations proved their ability to be used as command controllers.Keywords: neural system, neural activity, electromyography, machine learning, muscle activity
format	article
author	Hamedi M Salleh SH Tan TS Ismail K Ali J Dee-Uam C Pavaganun C Yupapin PP
author_facet	Hamedi M Salleh SH Tan TS Ismail K Ali J Dee-Uam C Pavaganun C Yupapin PP
author_sort	Hamedi M
title	Human facial neural activities and gesture recognition for machine-interfacing applications
title_short	Human facial neural activities and gesture recognition for machine-interfacing applications
title_full	Human facial neural activities and gesture recognition for machine-interfacing applications
title_fullStr	Human facial neural activities and gesture recognition for machine-interfacing applications
title_full_unstemmed	Human facial neural activities and gesture recognition for machine-interfacing applications
title_sort	human facial neural activities and gesture recognition for machine-interfacing applications
publisher	Dove Medical Press
publishDate	2011
url	https://doaj.org/article/87c1960a84d243f8a96116f8b76b6122
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Human facial neural activities and gesture recognition for machine-interfacing applications

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