Original title: Stress Detection On Non-Eeg Physiolog Data
Authors: Jindra, Jakub
Document type: Papers
Language: cze
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
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 %.
Keywords: artificial intelligence; decision trees; detection; machine learning; Non–EEG detection; physiological signals; Stress
Host item entry: Proceedings of the 25st Conference STUDENT EEICT 2019, ISBN 978-80-214-5735-5

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: http://hdl.handle.net/11012/186653

Permalink: http://www.nusl.cz/ntk/nusl-414555


The record appears in these collections:
Universities and colleges > Public universities > Brno University of Technology
Conference materials > Papers
 Record created 2020-07-11, last modified 2021-08-22


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