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Authors: David Sommer 1 ; Martin Golz 2 ; Udo Trutschel 3 and Dave Edwards 4

Affiliations: 1 University of Applied Sciences Schmalkalden, Faculty of Computer Science, Germany ; 2 Fachhochschule Schmalkalden - University Of Applied Sciences, Germany ; 3 Circadian, United States ; 4 Caterpillar Inc., United States

ISBN: 978-989-8111-66-1

Keyword(s): Hypovigilance, EEG, EOG, PERCLOS, Data Fusion, Support-Vector Machines, Driving Simulation.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Enterprise Information Systems ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Theory and Methods

Abstract: Robust and reliable determination of hypovigilance is required in many areas, particularly transportation. Here, new products of Fatigue Monitoring Technologies (FMT) emerge. Their development and assessment requires an independent reference standard of driver’s hypovigilance. Until recently most approaches utilized electrooculography (EOG) and electroencephalography (EEG) combined to descriptive statistics of a few time or spectral domain features, like e.g. power spectral densities (PSD) averaged in four to six spectral bands. Here we present a more general approach of data fusion of many features utilizing computational intelligence methods, like e.g. Support-Vector Machines (SVM). For simplicity, two classes were discriminated: slight and strong hypovigilance. Validation was performed by independent class labels which were assessed from Karolinska Sleepiness Scale (KSS) and from variation of lane deviation (VLD). The first is a measure of subjectively self-experienced hypovigilanc e, whereas the second is an objective measure of performance decrements. 16 young volunteers participated in overnight experiments in our real car driving simulation lab. Results were compared with PERCLOS (percentage of eye closure), an oculomotoric variable utilized in several FMT systems. We conclude that EEG and EOG biosignals are substantially more suited to assess driver’s hypovigilance than the PERCLOS biosignal. In addition, computational intelligence performed better when objective class labels were used instead of subjective class labels. (More)

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Paper citation in several formats:
Sommer D.; Golz M.; Trutschel U.; Edwards D. and (2009). DETERMINATION OF DRIVER’S HYPOVIGILANCE FROM BIOSIGNALS.In Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-8111-66-1, pages 130-135. DOI: 10.5220/0001746201300135

@conference{icaart09,
author={David Sommer and Martin Golz and Udo Trutschel and Dave Edwards},
title={DETERMINATION OF DRIVER’S HYPOVIGILANCE FROM BIOSIGNALS},
booktitle={Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2009},
pages={130-135},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001746201300135},
isbn={978-989-8111-66-1},
}

TY - CONF

JO - Proceedings of the International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - DETERMINATION OF DRIVER’S HYPOVIGILANCE FROM BIOSIGNALS
SN - 978-989-8111-66-1
AU - Sommer, D.
AU - Golz, M.
AU - Trutschel, U.
AU - Edwards, D.
PY - 2009
SP - 130
EP - 135
DO - 10.5220/0001746201300135

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