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Estimating Stress in Online Meetings by Remote Physiological Signal and Behavioral Features

dc.contributor.authorSun, Zhaodong
dc.contributor.authorVedernikov, Alexander
dc.contributor.authorKykyri, Virpi-Liisa
dc.contributor.authorPohjola, Mikko
dc.contributor.authorNokia, Miriam
dc.contributor.authorLi, Xiaobai
dc.date.accessioned2023-04-26T08:47:28Z
dc.date.available2023-04-26T08:47:28Z
dc.date.issued2022
dc.identifier.citationSun, Z., Vedernikov, A., Kykyri, V.-L., Pohjola, M., Nokia, M., & Li, X. (2022). Estimating Stress in Online Meetings by Remote Physiological Signal and Behavioral Features. In <i>UbiComp/ISWC '22 Adjunct : Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers</i> (pp. 216-220). ACM. <a href="https://doi.org/10.1145/3544793.3563406" target="_blank">https://doi.org/10.1145/3544793.3563406</a>
dc.identifier.otherCONVID_182921128
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/86616
dc.description.abstractWork stress impacts people’s daily lives. Their well-being can be improved if the stress is monitored and addressed in time. Attaching physiological sensors are used for such stress monitoring and analysis. Such approach is feasible only when the person is physically presented. Due to the transfer of the life from offline to online, caused by the COVID-19 pandemic, remote stress measurement is of high importance. This study investigated the feasibility of estimating participants’ stress levels based on remote physiological signal features (rPPG) and behavioral features (facial expression and motion) obtained from facial videos recorded during online video meetings. Remote physiological signal features provided higher accuracy of stress estimation (78.75%) as compared to those based on motion (70.00%) and facial expression (73.75%) features. Moreover, the fusion of behavioral and remote physiological signal features increased the accuracy of stress estimation up to 82.50%.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherACM
dc.relation.ispartofUbiComp/ISWC '22 Adjunct : Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers
dc.rightsCC BY 4.0
dc.subject.otherstress estimation
dc.subject.otherremote photoplethysmography
dc.subject.otherfacial expression
dc.subject.otherhead pose
dc.subject.othereye gaze
dc.titleEstimating Stress in Online Meetings by Remote Physiological Signal and Behavioral Features
dc.typeconference paper
dc.identifier.urnURN:NBN:fi:jyu-202304262723
dc.contributor.laitosKasvatustieteiden ja psykologian tiedekuntafi
dc.contributor.laitosPsykologian laitosfi
dc.contributor.laitosFaculty of Education and Psychologyen
dc.contributor.laitosDepartment of Psychologyen
dc.contributor.oppiainePsykologiafi
dc.contributor.oppiaineMonitieteinen aivotutkimuskeskusfi
dc.contributor.oppiaineHyvinvoinnin tutkimuksen yhteisöfi
dc.contributor.oppiainePsychologyen
dc.contributor.oppiaineCentre for Interdisciplinary Brain Researchen
dc.contributor.oppiaineSchool of Wellbeingen
dc.type.urihttp://purl.org/eprint/type/ConferencePaper
dc.relation.isbn978-1-4503-9423-9
dc.type.coarhttp://purl.org/coar/resource_type/c_5794
dc.description.reviewstatuspeerReviewed
dc.format.pagerange216-220
dc.type.versionpublishedVersion
dc.rights.copyright© 2022 Copyright held by the owner/author(s).
dc.rights.accesslevelopenAccessfi
dc.type.publicationconferenceObject
dc.relation.conferenceACM International Joint Conference on Pervasive and Ubiquitous Computing
dc.relation.grantnumber200337
dc.subject.ysostressi
dc.subject.ysosyke
dc.subject.ysosykemittarit
dc.subject.ysoetäkokoukset
dc.subject.ysoetäseuranta
dc.subject.ysoilmeet
dc.subject.ysokatseenseuranta
dc.subject.ysopsykofysiologia
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p133
jyx.subject.urihttp://www.yso.fi/onto/yso/p3751
jyx.subject.urihttp://www.yso.fi/onto/yso/p12342
jyx.subject.urihttp://www.yso.fi/onto/yso/p28060
jyx.subject.urihttp://www.yso.fi/onto/yso/p34193
jyx.subject.urihttp://www.yso.fi/onto/yso/p7717
jyx.subject.urihttp://www.yso.fi/onto/yso/p37956
jyx.subject.urihttp://www.yso.fi/onto/yso/p7543
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1145/3544793.3563406
dc.relation.funderFinnish Work Environment Funden
dc.relation.funderTyösuojelurahastofi
jyx.fundingprogramOthersen
jyx.fundingprogramMuutfi
jyx.fundinginformationThe study was supported by the Finnish Work Environment Fund (Project 200414 and 200337) and the Academy of Finland (Project 323287 and 345948). The authors also acknowledge CSC-IT Center for Science, Finland, for providing computational resources.
dc.type.okmA4


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