このエントリーをはてなブックマークに追加


ID 63838
FullText URL
fulltext.pdf 2.08 MB
Author
Murata, Atsuo Department of Intelligent Mechanical Systems, Graduate School of Natural Science and Technology, Okayama University Kaken ID publons researchmap
Doi, Toshihisa Department of Intelligent Mechanical Systems, Graduate School of Natural Science and Technology, Okayama University ORCID Kaken ID researchmap
Karwowski, Waldemar Department of Engineering and Management Systems, University of Central Florida
Abstract
It has been reported that many crashes are caused by drowsiness. Thus, it is critical to predict the occurrence of severe drowsiness that may result in a crash by means of an effective measure. The aim of this study was to investigate whether percentage closure (PERCLOS) of 70% was useful for evaluating drowsiness level of individual drivers and preventing crashes caused by drowsy driving using a driving simulator system. The first experiment measured PERCLOS70 during both aroused and drowsy states in a driving simulator task and investigated how PERCLOS70 changes when a participant fell asleep. In the second experiment, we measured PERCLOS70 and investigated the relation between PERCLOS70 and Karolinska Sleepiness Scale (KSS) ratings during a simulated driving task. The aggregated mean PERCLOS70 was significantly higher when participants fell asleep than when they were aroused. This tendency was also observed for individual participants. The aggregated mean PERCLOS70 was found to be sensitive to changes in KSS scores and increased with increasing KSS score. Linear trend analysis revealed a significant increasing trend for PERCLOS70 as a function of the KSS rating. This tendency was also observed for individual participants. PERCLOS70 was found to be sensitive to the drowsiness level both for data aggregated across all participants and data for individual participants. The main findings of the two experiments reported herein suggest that PERCLOS70 can be used effectively to evaluate drowsiness of individual drivers and prevent crashes caused by drowsy driving.
Keywords
Computer crashes
Sensitivity
Particle measurements
Atmospheric measurements
Eyelids
Task analysis
Data aggregation
Arousal level
drowsiness
PERCLOS70
Karolinska sleepiness scale
trend analysis
Published Date
2022
Publication Title
IEEE ACCESS
Volume
volume10
Publisher
IEEE - Inst Electrical Electronics Engineers Inc
Start Page
70806
End Page
70814
ISSN
2169-3536
Content Type
Journal Article
language
English
OAI-PMH Set
岡山大学
File Version
publisher
DOI
Web of Science KeyUT
Related Url
isVersionOf https://doi.org/10.1109/ACCESS.2022.3187995
License
https://creativecommons.org/licenses/by/4.0/