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dc.contributor.authorDe Arriba Perez, Francisco 
dc.contributor.authorSantos Gago, Juan Manuel 
dc.contributor.authorCaeiro Rodríguez, Manuel 
dc.contributor.authorRamos Merino, Mateo 
dc.date.accessioned2024-01-31T10:55:11Z
dc.date.available2024-01-31T10:55:11Z
dc.date.issued2019
dc.identifier.citationJournal of Ambient Intelligence and Humanized Computing, 10(12): 4925-4945 (2019)spa
dc.identifier.issn18685137
dc.identifier.issn18685145
dc.identifier.urihttp://hdl.handle.net/11093/5887
dc.description.abstractThis paper discusses the possibility of detecting personal stress making use of popular wearable devices available in the market. Different instruments found in the literature to measure stress-related features are reviewed, distinguishing between subjective tests and mechanisms supported by the analysis of physiological signals from clinical devices. Taking them as a reference, a solution to estimate stress based on the use of commercial-off-the-shelf wrist wearables and machine learning techniques is described. A mobile app was developed to induce stress in a uniform and systematic way. The app implements well-known stress inducers, such as the Paced Auditory Serial Addition Test, the Stroop Color-Word Interference Test, and a hyperventilation activity. Wearables are used to collect physiological data used to train classifiers that provide estimations on personal stress levels. The solution has been validated in an experiment involving 19 subjects, offering an average accuracy and F-measures close to 0.99 in an individual model and an accuracy and F-measure close to 0.85 in a global 2-level classifier model. Stress can be a worrying problem in different scenarios, such as in educational settings. Thus, the last part of the paper describes the proposal of a set of stress related indicators aimed to support the management of stress over time in such settings.spa
dc.description.sponsorshipAgencia Estatal de Investigación | Ref. TIN2016-80515-Rspa
dc.description.sponsorshipUniversidade de Vigospa
dc.language.isoengspa
dc.publisherJournal of Ambient Intelligence and Humanized Computingspa
dc.relationinfo:eu-repo/grantAgreement/AEI//Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2016-80515-R/ES
dc.rightsCopyright © 2019, Springer-Verlag GmbH Germany, part of Springer Nature
dc.titleStudy of stress detection and proposal of stress-related features using commercial-off-the-shelf wrist wearablesen
dc.typearticlespa
dc.rights.accessRightsopenAccessspa
dc.identifier.doi10.1007/s12652-019-01188-3
dc.identifier.editorhttp://link.springer.com/10.1007/s12652-019-01188-3spa
dc.publisher.departamentoEnxeñaría telemáticaspa
dc.publisher.grupoinvestigacionGrupo de Tecnoloxías da Informaciónspa
dc.publisher.grupoinvestigacionGIST (Grupo de Enxeñería de Sistemas Telemáticos)spa
dc.subject.unesco1203.10 Enseñanza Con Ayuda de Ordenadorspa
dc.subject.unesco3304.12 Dispositivos de Controlspa
dc.date.updated2024-01-29T17:49:53Z
dc.computerCitationpub_title=Journal of Ambient Intelligence and Humanized Computing|volume=10|journal_number=12|start_pag=4925|end_pag=4945spa


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