Physiological signal-based drowsiness detection using machine learning: Singular and hybrid signal approaches

Hasan, MD Mahmudul and Watling, Christopher N. ORCID: and Larue, Gregoire S. (2021) Physiological signal-based drowsiness detection using machine learning: Singular and hybrid signal approaches. Journal of Safety Research, 80. pp. 215-225. ISSN 0022-4375

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Introduction: Drowsiness is one of the main contributors to road-related crashes and fatalities worldwide. To address this pressing global issue, researchers are continuing to develop driver drowsiness detection systems which utilise a variety of measures. However, most research on drowsiness detection uses approaches based on a singular metric and, as a result, fail to attain satisfactory reliability and validity to be implemented in vehicles. Method: This study examines the utility of drowsiness detection based on singular and a hybrid approach. This approach considered a range of metrics from three physiological signals – electroencephalography (EEG), electrooculography (EOG), and electrocardiography (ECG) – and used subjective sleepiness indices (assessed via the Karolinska Sleepiness Scale) as ground truth. The methodology consisted of signal recording with a psychomotor vigilance test (PVT), pre-processing, extracting, and determining the important features from the physiological signals for drowsiness detection. Finally, four supervised machine learning models were developed based on the subjective sleepiness responses using the extracted physiological features to detect drowsiness levels. Results: The results illustrate that the singular physiological measures show a specific performance metric pattern, with higher sensitivity and lower specificity or vice versa. In contrast, the hybrid biosignal-based models provide a better performance profile, reducing the disparity between the two metrics. Conclusions: The outcome of the study indicates that the selected features provided higher performance in the hybrid approaches than the singular approaches, which could be useful for future research implications. Practical Applications: Use of a hybrid approaches seems warranted to improve in-vehicle driver drowsiness detection system. Practical applications will need to consider factors such as intrusiveness, ergonomics, cost-effectiveness, and user-friendliness of any driver drowsiness detection system.

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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 06:53
Last Modified: 31 Mar 2022 23:23
Uncontrolled Keywords: Accuracy; Drowsiness; Features; Ground truth; Machine learning; Physiological signals; Sensitivity; Specificity
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
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