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 |
|---|---|
| Title of host publication | Proceedings of the SICE Annual Conference |
| Pages | 1766-1771 |
| Number of pages | 6 |
| Publication status | Published - 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 |
|---|---|
| Country | Japan |
| City | Nagoya |
| Period | 9/14/13 → 9/17/13 |
Fingerprint
Keywords
- Behavioral measure
- Drowsiness
- Logistic regression model
- Physiological measure
- Traffic accident
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Computer Science Applications
Cite this
Verification of physiological or behavioral evaluation measures suitable for predicting drivers' drowsiness. / Murata, Atsuo; Koriyama, Taiga; Ohkubo, Yutaka; Moriwaka, Makoto; Hayami, Takehito.
Proceedings of the SICE Annual Conference. 2013. p. 1766-1771.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Verification of physiological or behavioral evaluation measures suitable for predicting drivers' drowsiness
AU - Murata, Atsuo
AU - Koriyama, Taiga
AU - Ohkubo, Yutaka
AU - Moriwaka, Makoto
AU - Hayami, Takehito
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Behavioral measure
KW - Drowsiness
KW - Logistic regression model
KW - Physiological measure
KW - Traffic accident
UR - http://www.scopus.com/inward/record.url?scp=84888598715&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84888598715&partnerID=8YFLogxK
M3 - Conference contribution
SP - 1766
EP - 1771
BT - Proceedings of the SICE Annual Conference
ER -