Predicting individual emotion from perception-based non-contact sensor big data

Abstract This study proposes a system for estimating individual emotions based on collected indoor environment data for human participants. At the first step, we develop wireless sensor nodes, which collect indoor environment data regarding human perception, for monitoring working environments. The...

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Autores principales:	Nobuyoshi Komuro, Tomoki Hashiguchi, Keita Hirai, Makoto Ichikawa
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5f24abb18d60444a81eb1396174f2738
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spelling	oai:doaj.org-article:5f24abb18d60444a81eb1396174f27382021-12-02T14:16:16ZPredicting individual emotion from perception-based non-contact sensor big data10.1038/s41598-021-81958-22045-2322https://doaj.org/article/5f24abb18d60444a81eb1396174f27382021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-81958-2https://doaj.org/toc/2045-2322Abstract This study proposes a system for estimating individual emotions based on collected indoor environment data for human participants. At the first step, we develop wireless sensor nodes, which collect indoor environment data regarding human perception, for monitoring working environments. The developed system collects indoor environment data obtained from the developed sensor nodes and the emotions data obtained from pulse and skin temperatures as big data. Then, the proposed system estimates individual emotions from collected indoor environment data. This study also investigates whether sensory data are effective for estimating individual emotions. Indoor environmental data obtained by developed sensors and emotions data obtained from vital data were logged over a period of 60 days. Emotions were estimated from indoor environmental data by machine learning method. The experimental results show that the proposed system achieves about 80% or more estimation correspondence by using multiple types of sensors, thereby demonstrating the effectiveness of the proposed system. Our obtained result that emotions can be determined with high accuracy from environmental data is a useful finding for future research approaches.Nobuyoshi KomuroTomoki HashiguchiKeita HiraiMakoto IchikawaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nobuyoshi Komuro Tomoki Hashiguchi Keita Hirai Makoto Ichikawa Predicting individual emotion from perception-based non-contact sensor big data
description	Abstract This study proposes a system for estimating individual emotions based on collected indoor environment data for human participants. At the first step, we develop wireless sensor nodes, which collect indoor environment data regarding human perception, for monitoring working environments. The developed system collects indoor environment data obtained from the developed sensor nodes and the emotions data obtained from pulse and skin temperatures as big data. Then, the proposed system estimates individual emotions from collected indoor environment data. This study also investigates whether sensory data are effective for estimating individual emotions. Indoor environmental data obtained by developed sensors and emotions data obtained from vital data were logged over a period of 60 days. Emotions were estimated from indoor environmental data by machine learning method. The experimental results show that the proposed system achieves about 80% or more estimation correspondence by using multiple types of sensors, thereby demonstrating the effectiveness of the proposed system. Our obtained result that emotions can be determined with high accuracy from environmental data is a useful finding for future research approaches.
format	article
author	Nobuyoshi Komuro Tomoki Hashiguchi Keita Hirai Makoto Ichikawa
author_facet	Nobuyoshi Komuro Tomoki Hashiguchi Keita Hirai Makoto Ichikawa
author_sort	Nobuyoshi Komuro
title	Predicting individual emotion from perception-based non-contact sensor big data
title_short	Predicting individual emotion from perception-based non-contact sensor big data
title_full	Predicting individual emotion from perception-based non-contact sensor big data
title_fullStr	Predicting individual emotion from perception-based non-contact sensor big data
title_full_unstemmed	Predicting individual emotion from perception-based non-contact sensor big data
title_sort	predicting individual emotion from perception-based non-contact sensor big data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5f24abb18d60444a81eb1396174f2738
work_keys_str_mv	AT nobuyoshikomuro predictingindividualemotionfromperceptionbasednoncontactsensorbigdata AT tomokihashiguchi predictingindividualemotionfromperceptionbasednoncontactsensorbigdata AT keitahirai predictingindividualemotionfromperceptionbasednoncontactsensorbigdata AT makotoichikawa predictingindividualemotionfromperceptionbasednoncontactsensorbigdata
_version_	1718391668093222912

Predicting individual emotion from perception-based non-contact sensor big data

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