Assessment of Driver Stress from Physiological Signals collected under Real-Time Semi-Urban Driving Scenarios

DOI

10.1080/18756891.2013.864478How to use a DOI?

Keywords

Wearable Driver Assist Systems, Physiological Signals, Affective State, Stress-Trends, Neural Networks

Abstract

Designing a wearable driver assist system requires extraction of relevant features from physiological signals like galvanic skin response and photoplethysmogram collected from automotive drivers during real-time driving. In the discussed case, four stress-classes were identified using cascade forward neural network (CASFNN) which performed consistently with minimal intra- and inter-subject variability. Task-induced stress-trends were tracked using Triggs’ Tracking Variable-based regression model with CASFNN configuration. The proposed framework will enable proactive initiation of rescue and relaxation procedures during accidents and emergencies.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Rajiv Ranjan Singh
AU  - Sailesh Conjeti
AU  - Rahul Banerjee
PY  - 2014
DA  - 2014/10/01
TI  - Assessment of Driver Stress from Physiological Signals collected under Real-Time Semi-Urban Driving Scenarios
JO  - International Journal of Computational Intelligence Systems
SP  - 909
EP  - 923
VL  - 7
IS  - 5
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2013.864478
DO  - 10.1080/18756891.2013.864478
ID  - Singh2014
ER  -