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dc.contributor.authorBogomolov, Andrey
dc.contributor.authorLepri, Bruno
dc.contributor.authorFerron, Michela
dc.contributor.authorPianesi, Fabio
dc.contributor.authorPentland, Alexander Sandy
dc.date.accessioned2021-12-20T19:27:24Z
dc.date.available2021-11-08T19:24:44Z
dc.date.available2021-12-20T19:27:24Z
dc.date.issued2014-03
dc.identifier.urihttps://hdl.handle.net/1721.1/137791.2
dc.description.abstractIn this paper we provide the evidence that daily stress can be reliably recognized based on human behavior metrics derived from the mobile phone activity (call log, sms log, bluetooth interactions). We introduce an original approach for feature extraction, selection, recognition model training and discuss the experimental results based on Random Forest and Gradient Boosted Machine algorithms. Random Forest based model showed low variance comparing to the GBM-based one, thus winning the bias-variance tradeoff and preventing over-fitting, given the noisy source data. Potential impact of the technology is reducing stress and enhancing subjective well-being for sustainable living. © 2014 IEEE.en_US
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.relation.isversionof10.1109/percomw.2014.6815230en_US
dc.rightsCreative Commons Attribution-Noncommercial-Share Alikeen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/en_US
dc.sourceOther repositoryen_US
dc.titlePervasive Stress Recognition for Sustainable Livingen_US
dc.typeArticleen_US
dc.identifier.citationBogomolov, Andrey, Lepri, Bruno, Ferron, Michela, Pianesi, Fabio and Pentland, Alex Sandy. 2014. "Pervasive Stress Recognition for Sustainable Living."en_US
dc.contributor.departmentMassachusetts Institute of Technology. Media Laboratoryen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/ConferencePaperen_US
eprint.statushttp://purl.org/eprint/status/NonPeerRevieweden_US
dc.date.updated2019-07-26T13:59:29Z
dspace.date.submission2019-07-26T13:59:30Z
mit.metadata.statusPublication Information Neededen_US


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