Publication detail
Stress detection on non-EEG physiolog. data
JINDRA, J. NĚMCOVÁ, A.
Original Title
Stress detection on non-EEG physiolog. data
English Title
Stress detection on non-EEG physiolog. data
Type
conference paper
Language
Czech
Original Abstract
Stress detection based on Non-EEG physiological data can be usefulfor monitoring dri-vers, pilots, workers,and other subjects, where standard EEG monitoring is unsuitable. This work usesNon-EEG database freelyavailable fromPhysionet. The database contains recordsof heart ra-te, 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 with25 features. The accuracy of the resulting model is approximately 93 %.
English abstract
Stress detection based on Non-EEG physiological data can be usefulfor monitoring dri-vers, pilots, workers,and other subjects, where standard EEG monitoring is unsuitable. This work usesNon-EEG database freelyavailable fromPhysionet. The database contains recordsof heart ra-te, 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 with25 features. The accuracy of the resulting model is approximately 93 %.
Keywords
Stress, detection, physiological signals, Non–EEG detection, artificial intelligence,machine learning,decision trees
Key words in English
Stress, detection, physiological signals, Non–EEG detection, artificial intelligence,machine learning,decision trees
Authors
JINDRA, J.; NĚMCOVÁ, A.
Released
25. 4. 2019
Publisher
Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Location
Brno
ISBN
978-80-214-5735-5
Book
Proceedings of the 25th Conference STUDENT EEICT 2019
Pages from
203
Pages to
206
Pages count
4
URL
BibTex
@inproceedings{BUT156802,
author="Jakub {Jindra} and Andrea {Němcová}",
title="Stress detection on non-EEG physiolog. data",
booktitle="Proceedings of the 25th Conference STUDENT EEICT 2019",
year="2019",
pages="203--206",
publisher="Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií",
address="Brno",
isbn="978-80-214-5735-5",
url="http://www.feec.vutbr.cz/EEICT/archiv/sborniky/EEICT_2019_sbornik.pdf"
}