Gazing as actual parameter for drowsiness assessment in driving simulators

Arthur Mourits Rumagit, Izzat Aulia Akbar, Mitaku Utsunomiya, Takamasa Morie, Tomohiko Igasaki*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)170-178
Number of pages9
JournalIndonesian Journal of Electrical Engineering and Computer Science
Issue number1
Publication statusPublished - Jan 2019
Externally publishedYes


  • Driving simulator
  • Drowsiness
  • Eye gaze tracker
  • Gazing
  • Support vector machine


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