Feature and Sensor Selection for Detection of Driver Stress

Simon Ollander, Christelle Godin, Sylvie Charbonnier, Aurélie Campagne

Abstract

This study presents a real-life application-based feature and sensor relevance analysis for detecting stress in drivers. Using the MIT Database for Stress Recognition in Automobile Drivers, the relevance of various physiological sensor signals and features for distinguishing the driver’s state have been analyzed. Features related to heart rate, skin conductivity, electromuscular activity, and respiration have been compared using filter and wrapper selection methods. For distinguishing rest from activity, relevant sensors have been found to be heart rate, skin conductivity, and respiration (giving up to 94.6 ± 1.9 % accuracy). For distinguishing low stress from high stress, relevant sensors have been found to be heart rate and respiration (giving up to 78.1±4.1 % accuracy). In both cases, a multi-user model that requires only a calibration from the user in rest, without prior knowledge of the user’s individual stress dynamics, resulted in a different optimal sensor and feature configuration, giving 87.3±2.8 % and 72.1±4.3 % accuracy respectively.

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Paper Citation


in Harvard Style

Ollander S., Godin C., Charbonnier S. and Campagne A. (2016). Feature and Sensor Selection for Detection of Driver Stress . In Proceedings of the 3rd International Conference on Physiological Computing Systems - Volume 1: PhyCS, ISBN 978-989-758-197-7, pages 115-122. DOI: 10.5220/0005973901150122


in Bibtex Style

@conference{phycs16,
author={Simon Ollander and Christelle Godin and Sylvie Charbonnier and Aurélie Campagne},
title={Feature and Sensor Selection for Detection of Driver Stress},
booktitle={Proceedings of the 3rd International Conference on Physiological Computing Systems - Volume 1: PhyCS,},
year={2016},
pages={115-122},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005973901150122},
isbn={978-989-758-197-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Physiological Computing Systems - Volume 1: PhyCS,
TI - Feature and Sensor Selection for Detection of Driver Stress
SN - 978-989-758-197-7
AU - Ollander S.
AU - Godin C.
AU - Charbonnier S.
AU - Campagne A.
PY - 2016
SP - 115
EP - 122
DO - 10.5220/0005973901150122