Driver drowsiness is associated with altered facial thermal patterns: Machine learning insights from a thermal imaging approach.

Autor: Aghamalizadeh A; Department of Occupational Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran., Mazloumi A; Department of Occupational Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran; School of Data Science, Nagoya City University, Nagoya, Japan. Electronic address: amazlomi@tums.ac.ir., Nikabadi A; Computer Engineering Department, Amirkabir University of Technology, Tehran, Iran., Nahvi A; Virtual Reality Laboratory, K. N. Toosi University of Technology, Tehran 19697-6449, Iran., Khanehshenas F; Department of Ergonomics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran., Ebrahimian S; Virtual Reality Laboratory, K. N. Toosi University of Technology, Tehran 19697-6449, Iran; Department of Technical Physics, University of Eastern Finland, 70210 Kuopio, Finland.
Jazyk: angličtina
Zdroj: Physiology & behavior [Physiol Behav] 2024 Sep 01; Vol. 283, pp. 114619. Date of Electronic Publication: 2024 Jun 23.
DOI: 10.1016/j.physbeh.2024.114619
Abstrakt: Driver drowsiness is a significant factor in road accidents. Thermal imaging has emerged as an effective tool for detecting drowsiness by enabling the analysis of facial thermal patterns. However, it is not clear which facial areas are most affected and correlate most strongly with drowsiness. This study examines the variations and importance of various facial areas and proposes an approach for detecting driver drowsiness. Twenty participants underwent tests in a driving simulator, and temperature changes in various facial regions were measured. The random forest method was employed to evaluate the importance of each facial region. The results revealed that temperature changes in the nasal area exhibited the highest value, while the eyes had the most correlated changes with drowsiness. Furthermore, drowsiness was classified with an accuracy of 88 % utilizing thermal variations in the facial region identified as the most important regions by the random forest feature importance model. These findings provide a comprehensive overview of facial thermal imaging for detecting driver drowsiness and introduce eye temperature as a novel and effective measure for investigating cognitive activities.
Competing Interests: Declaration of competing interest The authors declare no conflict of interest.
(Copyright © 2024. Published by Elsevier Inc.)
Databáze: MEDLINE
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