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dc.contributor.authorZepf, Sebastian
dc.contributor.authorEl Haouij, Neska
dc.contributor.authorLee, Jinmo
dc.contributor.authorGhandeharioun, Asma
dc.contributor.authorHernandez, Javier
dc.contributor.authorPicard, Rosalind W.
dc.date.accessioned2021-11-01T18:00:25Z
dc.date.available2021-11-01T18:00:25Z
dc.date.issued2020-07-07
dc.identifier.urihttps://hdl.handle.net/1721.1/137017
dc.description.abstract© 2020 Owner/Author. Driving can occupy a considerable part of our daily lives and is often associated with high levels of stress. Motivated by the effectiveness of controlled breathing, this work studies the potential use of breathing interventions while driving to help manage stress. In particular, we implemented and evaluated a closed-loop system that monitored the breathing rate of drivers in real-time and delivered either a conscious or an unconscious personalized acoustic breathing guide whenever needed. In a study with 24 participants, we observed that conscious interventions more effectively reduced the breathing rate but also increased the number of driving mistakes. We observed that prior driving experience as well as personality are significantly associated with the effect of the interventions, which highlights the importance of considering user profiles for in-car stress management interventions.en_US
dc.language.isoen
dc.publisherACMen_US
dc.relation.isversionof10.1145/3340631.3394854en_US
dc.rightsCreative Commons Attribution 4.0 International licenseen_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.sourceACMen_US
dc.titleStudying Personalized Just-in-time Auditory Breathing Guides and Potential Safety Implications during Simulated Drivingen_US
dc.typeArticleen_US
dc.identifier.citationZepf, Sebastian, El Haouij, Neska, Lee, Jinmo, Ghandeharioun, Asma, Hernandez, Javier et al. 2020. "Studying Personalized Just-in-time Auditory Breathing Guides and Potential Safety Implications during Simulated Driving." UMAP 2020 - Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalization.
dc.contributor.departmentMassachusetts Institute of Technology. Media Laboratory
dc.relation.journalUMAP 2020 - Proceedings of the 28th ACM Conference on User Modeling, Adaptation and Personalizationen_US
dc.eprint.versionFinal published versionen_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2021-07-06T14:40:31Z
dspace.orderedauthorsZepf, S; El Haouij, N; Lee, J; Ghandeharioun, A; Hernandez, J; Picard, RWen_US
dspace.date.submission2021-07-06T14:40:33Z
mit.licensePUBLISHER_CC
mit.metadata.statusAuthority Work and Publication Information Neededen_US


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