Gazing as actual parameter for drowsiness assessment in driving simulators

Many traffic accidents are due to drowsy driving. However, to date, only a few studies have been conducted on the gazing properties related to drowsiness. This study was conducted with the objective of estimating the relationship between gazing properties and drowsiness in three facial expression ev...

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Main Authors: Rumagit, Arthur Mourits (Author), Akbar, Izzat Aulia (Author), Utsunomiya, Mitaku (Author), Morie, Takamasa (Author), Igasaki, Tomohiko (Author)
Other Authors: Japan Society for the Promotion of Science (Contributor)
Format: EJournal Article
Published: Institute of Advanced Engineering and Science, 2019-01-01.
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100 1 0 |a Rumagit, Arthur Mourits  |e author 
100 1 0 |a Japan Society for the Promotion of Science  |e contributor 
700 1 0 |a Akbar, Izzat Aulia  |e author 
700 1 0 |a Utsunomiya, Mitaku  |e author 
700 1 0 |a Morie, Takamasa  |e author 
700 1 0 |a Igasaki, Tomohiko  |e author 
245 0 0 |a Gazing as actual parameter for drowsiness assessment in driving simulators 
260 |b Institute of Advanced Engineering and Science,   |c 2019-01-01. 
500 |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/15272 
520 |a Many traffic accidents are due to drowsy driving. However, to date, only a few studies have been conducted on the gazing properties related to drowsiness. This study was conducted with the objective of estimating the relationship between gazing properties and drowsiness in three facial expression evaluation (FEE) categories: alert (FEE = 0), lightly drowsy (FEE = 1−2), heavily drowsy (FEE = 3−4). Drowsiness was investigated based on these eye-gazing properties by analyzing the gazing signal utilizing an eye gaze tracker and FEE in a driving simulator environment. The results obtained indicate that gazing properties have significant differences among the three drowsiness conditions, with p < 0.001 in a Kruskal-Wallis test. Furthermore, the overall classification accuracy of the three drowsiness conditions based on gazing properties using a support vector machine was 76.3%. This indicates that our proposed gazing properties can be used to quantitatively assess drowsiness. 
540 |a Copyright (c) 2018 Institute of Advanced Engineering and Science 
540 |a http://creativecommons.org/licenses/by-nc/4.0 
546 |a eng 
690 |a biological signal processing; transportation safety and security 
690 |a drowsiness; gazing; eye gaze tracker; driving simulator; support vector machine 
655 7 |a info:eu-repo/semantics/article  |2 local 
655 7 |a info:eu-repo/semantics/publishedVersion  |2 local 
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786 0 |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 13, No 1: January 2019; 170-178 
786 0 |n 2502-4760 
786 0 |n 2502-4752 
786 0 |n 10.11591/ijeecs.v13.i1 
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