Estimation of drowsiness level based on eyelid closure and heart rate variability.
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.
Tsuchida
Ayumi
A
Graduate school of Information Science and Technology, Aichi Prefectural University, Aichi 480-1198, Japan. a.tsuchida@bme.ist.aichi-pu.ac.jp
Bhuiyan
Md
M
Oguri
Koji
K
D000063:Accidents, Traffic / Q000517:prevention & control*; D000465:Algorithms; D001334:Automobile Driving; D001767:Blinking; D003198:Computer Simulation; D003199:Computer Systems; D003625:Data Collection / Q000295:instrumentation* / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006339:Heart Rate; D006801:Humans; D008991:Monitoring, Physiologic / Q000295:instrumentation* / Q000379:methods*; D011597:Psychomotor Performance / Q000502:physiology; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D012894:Sleep Stages; D014741:Video Recording; D014851:Wakefulness