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GSR signals features extraction for emotion recognition

Over the years, the recognition of emotion has become more efficient, diverse, and easily accessible. In general, emotion recognition is conducted in four main steps which are signal acquisition, preprocessing, feature extraction, and classification. Galvanic skin response (GSR) is the autonomic act...

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Main Authors:	Kipli, Kuryati, Abdul Latip, Aisya Amelia, Lias, Kasumawati, Bateni, Norazlina, Mohamad Yusoff, Salmah, Tajudin, Nurul Mirza Afiqah, A. Jalil, M., Ray, Kanad, Kaiser, M. Shamim, Mahmud, Mufti
Format:	Book Section
Published:	Springer Science and Business Media Deutschland GmbH 2022
Subjects:	Q Science (General)
Online Access:	http://eprints.utm.my/id/eprint/101092/ http://dx.doi.org/10.1007/978-981-16-8826-3_28
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id	my.utm.101092
record_format	eprints
spelling	my.utm.1010922023-06-01T07:31:58Z http://eprints.utm.my/id/eprint/101092/ GSR signals features extraction for emotion recognition Kipli, Kuryati Abdul Latip, Aisya Amelia Lias, Kasumawati Bateni, Norazlina Mohamad Yusoff, Salmah Tajudin, Nurul Mirza Afiqah A. Jalil, M. Ray, Kanad Kaiser, M. Shamim Mahmud, Mufti Q Science (General) Over the years, the recognition of emotion has become more efficient, diverse, and easily accessible. In general, emotion recognition is conducted in four main steps which are signal acquisition, preprocessing, feature extraction, and classification. Galvanic skin response (GSR) is the autonomic activation of sweat glands in the skin when an individual gets triggered through emotional stimulation. The paper provides an overview of emotion recognition, GSR signals, and how GSR signals are analyzed for emotion recognition. The focus of this research is on the performance of feature extraction of GSR signals. Therefore, related sources were identified using combinations of keywords and terms such as feature extraction, emotion recognition, and galvanic skin response. Existing emotion recognition methods were investigated which focused more on the different feature extraction methods. Research conducted has shown that feature extraction method in time–frequency domain has the best accuracy rate overall compared to time domain and frequency domain. Current GSR-based technology also has the potential to be improved more toward the implementation of a more efficient and reliable emotion recognition system. Springer Science and Business Media Deutschland GmbH 2022 Book Section PeerReviewed Kipli, Kuryati and Abdul Latip, Aisya Amelia and Lias, Kasumawati and Bateni, Norazlina and Mohamad Yusoff, Salmah and Tajudin, Nurul Mirza Afiqah and A. Jalil, M. and Ray, Kanad and Kaiser, M. Shamim and Mahmud, Mufti (2022) GSR signals features extraction for emotion recognition. In: Proceedings of Trends in Electronics and Health Informatics TEHI 2021. Lecture Notes in Networks and Systems, 376 (NA). Springer Science and Business Media Deutschland GmbH, Singapore, pp. 329-338. ISBN 978-981168825-6 http://dx.doi.org/10.1007/978-981-16-8826-3_28 DOI:10.1007/978-981-16-8826-3_28
institution	Universiti Teknologi Malaysia
building	UTM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Teknologi Malaysia
content_source	UTM Institutional Repository
url_provider	http://eprints.utm.my/
topic	Q Science (General)
spellingShingle	Q Science (General) Kipli, Kuryati Abdul Latip, Aisya Amelia Lias, Kasumawati Bateni, Norazlina Mohamad Yusoff, Salmah Tajudin, Nurul Mirza Afiqah A. Jalil, M. Ray, Kanad Kaiser, M. Shamim Mahmud, Mufti GSR signals features extraction for emotion recognition
description	Over the years, the recognition of emotion has become more efficient, diverse, and easily accessible. In general, emotion recognition is conducted in four main steps which are signal acquisition, preprocessing, feature extraction, and classification. Galvanic skin response (GSR) is the autonomic activation of sweat glands in the skin when an individual gets triggered through emotional stimulation. The paper provides an overview of emotion recognition, GSR signals, and how GSR signals are analyzed for emotion recognition. The focus of this research is on the performance of feature extraction of GSR signals. Therefore, related sources were identified using combinations of keywords and terms such as feature extraction, emotion recognition, and galvanic skin response. Existing emotion recognition methods were investigated which focused more on the different feature extraction methods. Research conducted has shown that feature extraction method in time–frequency domain has the best accuracy rate overall compared to time domain and frequency domain. Current GSR-based technology also has the potential to be improved more toward the implementation of a more efficient and reliable emotion recognition system.
format	Book Section
author	Kipli, Kuryati Abdul Latip, Aisya Amelia Lias, Kasumawati Bateni, Norazlina Mohamad Yusoff, Salmah Tajudin, Nurul Mirza Afiqah A. Jalil, M. Ray, Kanad Kaiser, M. Shamim Mahmud, Mufti
author_facet	Kipli, Kuryati Abdul Latip, Aisya Amelia Lias, Kasumawati Bateni, Norazlina Mohamad Yusoff, Salmah Tajudin, Nurul Mirza Afiqah A. Jalil, M. Ray, Kanad Kaiser, M. Shamim Mahmud, Mufti
author_sort	Kipli, Kuryati
title	GSR signals features extraction for emotion recognition
title_short	GSR signals features extraction for emotion recognition
title_full	GSR signals features extraction for emotion recognition
title_fullStr	GSR signals features extraction for emotion recognition
title_full_unstemmed	GSR signals features extraction for emotion recognition
title_sort	gsr signals features extraction for emotion recognition
publisher	Springer Science and Business Media Deutschland GmbH
publishDate	2022
url	http://eprints.utm.my/id/eprint/101092/ http://dx.doi.org/10.1007/978-981-16-8826-3_28
_version_	1768006607734571008
score	13.211869

GSR signals features extraction for emotion recognition

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