Sensitivity and specificity of the driver sleepiness detection methods using physiological signals: a systematic review

Watling, Christopher N. ORCID: https://orcid.org/0000-0002-1440-2401 and Hasan, Md Mahmudul and Larue, Gregoire S. ORCID: https://orcid.org/0000-0001-8564-9084 (2021) Sensitivity and specificity of the driver sleepiness detection methods using physiological signals: a systematic review. Accident Analysis and Prevention, 150:105900. pp. 1-11. ISSN 0001-4575

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Abstract

Driver sleepiness is a major contributor to road crashes. A system that monitors and warns the driver at a certain, critical level of arousal, could aid in reducing sleep-related crashes. To determine how driver sleepiness detection systems perform, a systematic review of the sensitivity and specificity outcomes was performed. In total, 21 studies were located that met inclusion criteria for the review. The range of sensitivity outcomes was between 39.0-98.8% and between 73.0-98.9% for specificity outcomes. There was considerable variation in the outcomes of the studies employing only one physiological measure (mono-signal approach), whereas, a poly-signal approach with multiple physiological signals resulted in more consistency with higher outcomes on both sensitivity and specificity metrics. Only six of the 21 studies had both sensitivity and specificity outcomes above 90.0%, which included mono- and poly-signal approaches. Moreover, increases in the number of features used in the sleepiness detection system did not result in higher sensitivity and specificity outcomes. Overall, there was considerable variability between the studies reviewed, including measures of ground truth, the features employed and the machine learning approach of the systems. A critical need for progressing any system is a revalidation of the system on a new sample of users. These aspects indicate considerable progress is needed with physiological-based driver sleepiness systems before they are at a sufficient standard to be deployed on-road.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: No Faculty
Faculty/School / Institute/Centre: No Faculty
Date Deposited: 07 Mar 2022 23:25
Last Modified: 30 Mar 2022 05:18
Uncontrolled Keywords: fatigue, drowsiness, driving, features, machine learning, ground truth, physiological sleepiness
Fields of Research (2008): 17 Psychology and Cognitive Sciences > 1702 Cognitive Sciences > 170201 Computer Perception, Memory and Attention
11 Medical and Health Sciences > 1117 Public Health and Health Services > 111799 Public Health and Health Services not elsewhere classified
17 Psychology and Cognitive Sciences > 1701 Psychology > 170101 Biological Psychology (Neuropsychology, Psychopharmacology, Physiological Psychology)
Fields of Research (2020): 42 HEALTH SCIENCES > 4206 Public health > 420604 Injury prevention
52 PSYCHOLOGY > 5202 Biological psychology > 520206 Psychophysiology
52 PSYCHOLOGY > 5204 Cognitive and computational psychology > 520404 Memory and attention
Socio-Economic Objectives (2008): C Society > 92 Health > 9204 Public Health (excl. Specific Population Health) > 920409 Injury Control
E Expanding Knowledge > 97 Expanding Knowledge > 970117 Expanding Knowledge in Psychology and Cognitive Sciences
Socio-Economic Objectives (2020): 28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280102 Expanding knowledge in the biological sciences
28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280121 Expanding knowledge in psychology
27 TRANSPORT > 2703 Ground transport > 270311 Road safety
Identification Number or DOI: https://doi.org/10.1016/j.aap.2020.105900
URI: http://eprints.usq.edu.au/id/eprint/46473

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