Stress Detection On Non-Eeg Physiolog Data

but.event.date25.04.2019cs
but.event.titleStudent EEICT 2019cs
dc.contributor.authorJindra, Jakub
dc.date.accessioned2020-04-16T07:19:30Z
dc.date.available2020-04-16T07:19:30Z
dc.date.issued2019cs
dc.description.abstractStress detection based on Non-EEG physiological data can be useful for monitoring drivers, pilots, workers, and other subjects, where standard EEG monitoring is unsuitable. This work uses Non-EEG database freely available from Physionet. The database contains records of heart rate, saturation of blood oxygen, motion, a conductance of skin and temperature. Model for automatic detection of stress was learned on these data. Best results were reached using a model of a decision tree with 25 features. The accuracy of the resulting model is approximately 93 %.en
dc.formattextcs
dc.format.extent203-206cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings of the 25st Conference STUDENT EEICT 2019. s. 203-206. ISBN 978-80-214-5735-5cs
dc.identifier.isbn978-80-214-5735-5
dc.identifier.urihttp://hdl.handle.net/11012/186653
dc.language.isocscs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings of the 25st Conference STUDENT EEICT 2019en
dc.relation.urihttp://www.feec.vutbr.cz/EEICT/cs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectStressen
dc.subjectdetectionen
dc.subjectphysiological signalsen
dc.subjectNon–EEG detectionen
dc.subjectartificial intelligenceen
dc.subjectmachine learningen
dc.subjectdecision treesen
dc.titleStress Detection On Non-Eeg Physiolog Dataen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
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