Abstract
Although many studies used psychophysiological measures such as blink, EEG, saccade, and heart rate to assess fatigue or drowsiness, no measures alone can be used reliably to assess drowsiness. The results of these studies must be integrated and effectively applied to the prevention of drowsy driving. To prevent drivers from driving under drowsy state and causing a disastrous traffic accident, not the gross tendency of reduced arousal level but the more accurate identification of timing when the drowsy state occurs is necessary. The aim of this study was to verify the effectiveness of physiological or behavioral evaluation measures suitable for predicting drivers' drowsiness 20s before drowsy driving situations occur. Drowsy states were predicted by applying multinomial logistic regression analysis to biological information. Thus, we made an attempt to establish a method to properly and timely warn drivers of drowsy state by EEG, heart rate variability, standard deviation of quantity of pedal operation, and mean tracking error.
| Original language | English |
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| Pages | 1766-1771 |
| Number of pages | 6 |
| Publication status | Published - Jan 1 2013 |
| Event | 2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013 - Nagoya, Japan Duration: Sep 14 2013 → Sep 17 2013 |
Other
| Other | 2013 52nd Annual Conference of the Society of Instrument and Control Engineers of Japan, SICE 2013 |
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| Country/Territory | Japan |
| City | Nagoya |
| Period | 9/14/13 → 9/17/13 |
Keywords
- Behavioral measure
- Drowsiness
- Logistic regression model
- Physiological measure
- Traffic accident
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications
- Electrical and Electronic Engineering
