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