Key result
Physiological-based driver monitoring systems utilizing EEG, ECG, EOG, and EMG demonstrate high potential and accuracy in detecting driver states such as fatigue, drowsiness, and alcohol intoxication.
Why the study?
Coupling non-physiological driver monitoring systems with physiological data analysis from EEG, EOG, ECG, and EMG can assess driver physical and emotional states and wellness to avoid traffic collisions.
Physiological-based driver monitoring systems utilizing signals like ECG, EEG, and EMG show high potential for accurate driver identification and alertness monitoring to prevent traffic collisions.
Answers are grounded in this paper and cite their sources.
Should not yet change driver monitoring practices; hypothesis-generating for multimodal biosensors and requires prospective validation trials.
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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Razak et al. (2022) conducted a review in Driver state monitoring (fatigue, drowsiness, alertness, drunk driving) (n=93). Physiological-based Driver Monitoring Systems was evaluated on Driver state detection accuracy (narrative review). Physiological-based driver monitoring systems utilizing EEG, ECG, EOG, and EMG demonstrate high potential and accuracy in detecting driver states such as fatigue, drowsiness, and alcohol intoxication.
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