Citation

Tsuchida A, Bhuiyan M, Oguri K. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2009; 2009: 2543-2546.

Affiliation

Graduate school of Information Science and Technology, Aichi Prefectural University, Aichi 480-1198, Japan. a.tsuchida@bme.ist.aichi-pu.ac.jp

Copyright

(Copyright © 2009, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/IEMBS.2009.5334766

PMID

19964977

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.

Language: en