@article{ref1,
title="Estimation of drowsiness level based on eyelid closure and heart rate variability",
journal="Conference proceedings - IEEE engineering in medicine and biology society",
year="2009",
author="Tsuchida, Ayumi and Bhuiyan, Md and Oguri, Koji",
volume="2009",
number="",
pages="2543-2546",
abstract="This paper presents a novel method that uses eyelid closure and heart rate variability to estimate the driver's drowsiness level. Laboratory experiments were conducted by using a proprietary driving simulator, which induced drowsiness among the test drivers. The purposes of these experiments were to obtain the electrocardiogram (ECG) and the eye-blink video sequences. Also the drivers were monitored through a video camera. The changes in facial expression of the drivers were used as a standard index of drowsiness level. Error-Correcting Output Coding (ECOC) was employed as a multi-class classifier to estimate the drowsiness level. We extended the ordinary ECOC using a loss function for decoding procedure to obtain class tendencies of each drowsiness level. We used the Loss-based Decoding ECOC (LD-ECOC) to classify the drowsiness level. As a result, we obtained an extraordinarily high accuracy for estimation of drowsiness level.<p /> <p>Language: en</p>",
language="en",
issn="1557-170X",
doi="10.1109/IEMBS.2009.5334766",
url="http://dx.doi.org/10.1109/IEMBS.2009.5334766"
}