Stress Detection On Non-Eeg Physiolog Data
| but.event.date | 25.04.2019 | cs |
| but.event.title | Student EEICT 2019 | cs |
| dc.contributor.author | Jindra, Jakub | |
| dc.date.accessioned | 2020-04-16T07:19:30Z | |
| dc.date.available | 2020-04-16T07:19:30Z | |
| dc.date.issued | 2019 | cs |
| dc.description.abstract | Stress 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.format | text | cs |
| dc.format.extent | 203-206 | cs |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.citation | Proceedings of the 25st Conference STUDENT EEICT 2019. s. 203-206. ISBN 978-80-214-5735-5 | cs |
| dc.identifier.isbn | 978-80-214-5735-5 | |
| dc.identifier.uri | http://hdl.handle.net/11012/186653 | |
| dc.language.iso | cs | cs |
| dc.publisher | Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií | cs |
| dc.relation.ispartof | Proceedings of the 25st Conference STUDENT EEICT 2019 | en |
| dc.relation.uri | http://www.feec.vutbr.cz/EEICT/ | cs |
| dc.rights | © Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií | cs |
| dc.rights.access | openAccess | en |
| dc.subject | Stress | en |
| dc.subject | detection | en |
| dc.subject | physiological signals | en |
| dc.subject | Non–EEG detection | en |
| dc.subject | artificial intelligence | en |
| dc.subject | machine learning | en |
| dc.subject | decision trees | en |
| dc.title | Stress Detection On Non-Eeg Physiolog Data | en |
| dc.type.driver | conferenceObject | en |
| dc.type.status | Peer-reviewed | en |
| dc.type.version | publishedVersion | en |
| eprints.affiliatedInstitution.department | Fakulta elektrotechniky a komunikačních technologií | cs |
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