Stress Detection On Non-Eeg Physiolog Data
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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 %.
KeywordsStress, detection, physiological signals, Non–EEG detection, artificial intelligence, machine learning, decision trees
Document typePeer reviewed
Document versionxmlui.vut.verze.Publishers's version
SourceProceedings of the 25st Conference STUDENT EEICT 2019. s. 203-206. ISBN 978-80-214-5735-5
- Student EEICT 2019