ESTIMATE VIGILANCE IN DRIVING SIMULATION BASED ON DETECTION OF LIGHT DROWSINESS

Hong-Jun Liu, Qing-Sheng Ren, Hong-Tao Lu

2010

Abstract

Avoiding fatal accidents caused by low vigilance level in driving is very important in our daily lives. Electroencephalography (EEG) has been proved very effective for measuring the level of vigilance. In this paper, we identify light drowsiness state from other states to estimate vigilance level decline by using support vector machine (SVM). Light drowsiness EEG is marked by alpha increasing to 50%. Alert EEG is marked by dominant beta activity and other EEG is labeled as sleep state. Samples of EEG data are trained in SVM program by using 4 features from each frequency band. Mutual information based feature selection method is used to reduce the dimension of features. The accuracy in classification of alert and light drowsiness reaches 91.5% on average.

Paper Citation

in Harvard Style

Liu H., Ren Q. and Lu H. (2010). ESTIMATE VIGILANCE IN DRIVING SIMULATION BASED ON DETECTION OF LIGHT DROWSINESS . In Proceedings of the First International Conference on Bioinformatics - Volume 1: BIOINFORMATICS, (BIOSTEC 2010) ISBN 978-989-674-019-1, pages 131-134. DOI: 10.5220/0002724201310134

in Bibtex Style

@conference{bioinformatics10,
author={Hong-Jun Liu and Qing-Sheng Ren and Hong-Tao Lu},
title={ESTIMATE VIGILANCE IN DRIVING SIMULATION BASED ON DETECTION OF LIGHT DROWSINESS},
booktitle={Proceedings of the First International Conference on Bioinformatics - Volume 1: BIOINFORMATICS, (BIOSTEC 2010)},
year={2010},
pages={131-134},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002724201310134},
isbn={978-989-674-019-1},
}

in EndNote Style

TY - CONF
JO - Proceedings of the First International Conference on Bioinformatics - Volume 1: BIOINFORMATICS, (BIOSTEC 2010)
TI - ESTIMATE VIGILANCE IN DRIVING SIMULATION BASED ON DETECTION OF LIGHT DROWSINESS
SN - 978-989-674-019-1
AU - Liu H.
AU - Ren Q.
AU - Lu H.
PY - 2010
SP - 131
EP - 134
DO - 10.5220/0002724201310134