Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/105708
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dc.contributorDepartment of Computing-
dc.creatorLi, CT-
dc.creatorCao, J-
dc.creatorLi, TMH-
dc.date.accessioned2024-04-15T07:36:03Z-
dc.date.available2024-04-15T07:36:03Z-
dc.identifier.isbn978-1-4503-4462-3-
dc.identifier.urihttp://hdl.handle.net/10397/105708-
dc.descriptionUbiComp '16: The 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, Heidelberg Germany, September 12 - 16, 2016en_US
dc.language.isoenen_US
dc.publisherAssociation for Computing Machineryen_US
dc.rightsCopyright is held by the owner/author(s). This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in UbiComp '16: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct, http://dx.doi.org/10.1145/2968219.2968309.en_US
dc.subjectEustressen_US
dc.subjectMHealthen_US
dc.subjectStressen_US
dc.subjectUbiquitous Computingen_US
dc.titleEustress or distress : an empirical study of perceived stress in everyday college lifeen_US
dc.typeConference Paperen_US
dc.identifier.spage1209-
dc.identifier.epage1217-
dc.identifier.doi10.1145/2968219.2968309-
dcterms.abstractEustress is literally the "good stress" that associated with positive feelings and health benefits. Previous studies focused on general stress, where the concept of eustress has been overlooked. This paper presents a novel approach towards stress recognition using data collected from wearable sensors, smartphones, and computers. The main goal is to determine if behavioral factors can help differentiate eustress from another kind of stress. We conducted a natural experiment to collect user smartphone and computer usage, heart rate and survey data in situ. By correlation and principle component analysis, a set of features could then be constructed. The performance was evaluated under leave-one-subject-out cross-validation, where the combined behavioral and physiological features enabled us to achieve 84.85% accuracy for general stress, 71.33% one kind of eustress as an urge for better performance, and 57.34% for eustress as a state of better mood. This work provided an encouraging result as an initial study for measuring eustress.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIn UbiComp '16 adjunct : proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing : September 12-16, 2016, Heidelberg, Germany, p. 1209-1217. New York : ACM, 2016-
dcterms.issued2016-
dc.identifier.scopus2-s2.0-84991080285-
dc.relation.ispartofbookUbiComp '16 adjunct : proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing : September 12-16, 2016, Heidelberg, Germany-
dc.relation.conferenceACM International Joint Conference on Pervasive and Ubiquitous Computing [UbiComp]-
dc.description.validate202402 bcch-
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberCOMP-1456en_US
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS9581473en_US
dc.description.oaCategoryGreen (AAM)en_US
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