start-ver=1.4
cd-journal=joma
no-vol=10
cd-vols=
no-issue=
article-no=
start-page=70806
end-page=70814
dt-received=
dt-revised=
dt-accepted=
dt-pub-year=2022
dt-pub=2022
dt-online=
en-article=
kn-article=
en-subject=
kn-subject=
en-title=
kn-title=Sensitivity of PERCLOS70 to Drowsiness Level: Effectiveness of PERCLOS70 to Prevent Crashes Caused by Drowsiness
en-subtitle=
kn-subtitle=
en-abstract=
kn-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.
en-copyright=
kn-copyright=

en-aut-name=MurataAtsuo
en-aut-sei=Murata
en-aut-mei=Atsuo
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=1
ORCID=

en-aut-name=DoiToshihisa
en-aut-sei=Doi
en-aut-mei=Toshihisa
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=2
ORCID=

en-aut-name=KarwowskiWaldemar
en-aut-sei=Karwowski
en-aut-mei=Waldemar
kn-aut-name=
kn-aut-sei=
kn-aut-mei=
aut-affil-num=3
ORCID=

affil-num=1
en-affil=Department of Intelligent Mechanical Systems, Graduate School of Natural Science and Technology, Okayama University
kn-affil=

affil-num=2
en-affil=Department of Intelligent Mechanical Systems, Graduate School of Natural Science and Technology, Okayama University
kn-affil=

affil-num=3
en-affil=Department of Engineering and Management Systems, University of Central Florida
kn-affil=
en-keyword=Computer crashes
kn-keyword=Computer crashes
en-keyword=Sensitivity
kn-keyword=Sensitivity
en-keyword=Particle measurements
kn-keyword=Particle measurements
en-keyword=Atmospheric measurements
kn-keyword=Atmospheric measurements
en-keyword=Eyelids
kn-keyword=Eyelids
en-keyword=Task analysis
kn-keyword=Task analysis
en-keyword=Data aggregation
kn-keyword=Data aggregation
en-keyword=Arousal level
kn-keyword=Arousal level
en-keyword=drowsiness
kn-keyword=drowsiness
en-keyword=PERCLOS70
kn-keyword=PERCLOS70
en-keyword=Karolinska sleepiness scale
kn-keyword=Karolinska sleepiness scale
en-keyword=trend analysis
kn-keyword=trend analysis
END