Assessing Driver Fatigue During Urban Traffic Congestion Using ECG Method

Gyulyev, Nizami; Galkin, Andrii; Schlosser, Tibor; Capayova, Silvia; Lobashov, Oleksii

doi:10.1007/978-3-031-05359-7_36

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…Hence, ECG data was used to acquire patterns for assessing fatigue of the driver based on the age, time spent in traffic congestion and pre-congestion state prior. The study revealed that 10-12% of drivers begin to feel fatigue after 7-10 min trapped in traffic congestion [50] Besides that, continuous noise may also induce driver fatigueness. An investigation based on EEG data was conducted to measure the changes of brain wave activity when induced with broadband noise of 40 dBA, 55 dBA and 75 dBA during a 1.5 -hour monotonous drive.…”

Section: Driver Fatigue Monitoringmentioning

confidence: 94%

Physiological-based Driver Monitoring Systems: A Scoping Review

et al. 2022

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A physiological-based driver monitoring system (DMS) has attracted research interest and has great potential for providing more accurate and reliable monitoring of the driver’s state during a driving experience. Many driving monitoring systems are driver behavior-based or vehicle-based. When these non-physiological based DMS are coupled with physiological-based data analysis from electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG), the physical and emotional state of the driver may also be assessed. Drivers’ wellness can also be monitored, and hence, traffic collisions can be avoided. This paper highlights work that has been published in the past five years related to physiological-based DMS. Specifically, we focused on the physiological indicators applied in DMS design and development. Work utilizing key physiological indicators related to driver identification, driver alertness, driver drowsiness, driver fatigue, and drunk driver is identified and described based on the PRISMA Extension for Scoping Reviews (PRISMA-Sc) Framework. The relationship between selected papers is visualized using keyword co-occurrence. Findings were presented using a narrative review approach based on classifications of DMS. Finally, the challenges of physiological-based DMS are highlighted in the conclusion. Doi: 10.28991/CEJ-2022-08-12-020 Full Text: PDF

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Section: Driver Fatigue Monitoringmentioning

confidence: 94%