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ID 19600
Eprint ID
19600
FullText URL
Author
Hiramatsu, Yasutaka
Abstract
The aim of this study was to propose a useful prediction method of drowsy state of drivers, so that the result is applicable to the development of ITS (Intelligent Transportation System) that can warn drivers of their low arousal state and to prevent driving under low arousal level from occurring. The EEG (electroencephalography) and ECG (electrocardiography) during a monotonous task was measured, and it was investigated how these measures change under the low arousal (drowsy) state. The EEG measurement was added to in order to monitor arousal level more the time series of mean power frequency of EEG was plotted on X-bar control chart. Heart rate variability (HRV) measure RRV3 were derived on the basis of R-R intervals (interbeat intervals) obtained from ECG. Using a Bayesian probability, we tried to predict the timing when the participant actually felt drowsy. As a result, the prediction accuracy differed by the state of participant. When the drowsiness of participant was remarkable, the prediction method was effective to some extent. On the other hand, the proposed method could not predict the drowsy state reliably when the participant did not feel drowsiness to a larger extent.
Published Date
2009-11-12
Publication Title
Proceedings : Fifth International Workshop on Computational Intelligence & Applications
Publication Title Alternative
IEEE SMC Hiroshima Chapter
Volume
volume2009
Issue
issue1
Publisher
IEEE SMC Hiroshima Chapter
Start Page
348
End Page
353
ISSN
1883-3977
NCID
BB00577064
Content Type
Conference Paper
language
英語
Copyright Holders
IEEE SMC Hiroshima Chapter
Event Title
5th International Workshop on Computational Intelligence & Applications IEEE SMC Hiroshima Chapter : IWCIA 2009
Event Location
東広島市
Event Location Alternative
Higashi-Hiroshima City
File Version
publisher
Refereed
True
Eprints Journal Name
IWCIA