@article{ref1,
title="Steering wheel behavior based estimation of fatigue",
journal="Proceedings of the ... international driving symposium on human factors in driver assessment, training and vehicle design",
year="2009",
author="Krajewski, Jarek and Sommer, David and Trutschel, Udo and Edwards, David and Golz, Martin",
volume="5",
number="",
pages="118-124",
abstract="This paper examined a steering behavior based fatigue monitoring system. The advantages of using steering behavior for detecting fatigue are that these systems measure continuously, cheaply, non-intrusively, and robustly even under extremely demanding environmental conditions. The expected fatigue induced changes in steering behavior are a pattern of slow drifting and fast corrective counter steering. Using advanced signal processing procedures for feature extraction, we computed 3 feature set in the time, frequency and state space domain (a total number of 1251 features) to capture fatigue impaired steering patterns. Each feature set was separately fed into 5 machine learning methods (e.g. Support Vector Machine, K-Nearest Neighbor). The outputs of each single classifier were combined to an ensemble classification value. Finally we combined the ensemble values of 3 feature subsets to a of meta-ensemble classification value. To validate the steering behavior analysis, driving samples are taken from a driving simulator during a sleep deprivation study (N=12). We yielded a recognition rate of 86.1% in classifying slight from strong fatigue.<p />",
language="",
issn="",
doi="",
url="http://dx.doi.org/"
}