Narrative review: Do spontaneous eye blink parameters provide a useful assessment of state drowsiness?

Cori, Jennifer M; Anderson, Clare; Soleimanloo, Shamsi Shekari; Jackson, Melinda L.; Howard, Mark

doi:10.1016/j.smrv.2019.03.004

Cited by 49 publications

(36 citation statements)

References 58 publications

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“…Finally, although SBR is considered a reliable indicator of central dopaminergic tone [3][4][5][6][7][8][9][10][11][12] , this parameter can be influenced by numerous factors (e.g. non-dopaminergic system activity, other nonspecific factors including the variable activation of afferent fibers originating from the cornea, the circadian conditions and the state of vigilance) [65][66][67] . Concerning the latter, although we did not perform an evaluation of the state of vigilance through standardized scales, we minimized this possible confounding factor by performing the experiments in a limited period of time, always visually and verbally checking the state of vigilance of the participants.…”

Section: Discussionmentioning

confidence: 99%

Painful stimulation increases spontaneous blink rate in healthy subjects

Paparella

Stefano

Fasolino

et al. 2020

Sci Rep

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Spontaneous blink rate is considered a biomarker of central dopaminergic activity. Recent evidence suggests that the central dopaminergic system plays a role in nociception. In the present study, we aimed to investigate whether pain modulates spontaneous blink rate in healthy subjects. We enrolled 15 participants. Spontaneous blink rate was quantified with an optoelectronic system before and after: (1) a painful laser stimulation, and (2) an acoustic startling stimulation. In control experiments, we investigated whether laser stimulation effects depended on stimulation intensity and whether laser stimulation induced any changes in the blink reflex recovery cycle. Finally, we investigated any relationship between spontaneous blink rate modification and pain modulation effect during the cold pressor test. Laser, but not acoustic, stimulation increased spontaneous blink rate. This effect was independent of stimulation intensity and negatively correlated with pain perception. No changes in trigeminal-facial reflex circuit excitability were elicited by laser stimulation. The cold pressor test also induced an increased spontaneous blink rate. Our study provides evidence on the role of dopamine in nociception and suggests that dopaminergic activity may be involved in pain modulation. These findings lay the groundwork for further investigations in patients with pathological conditions characterized by dopaminergic deficit and pain.

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Section: Discussionmentioning

confidence: 99%

Painful stimulation increases spontaneous blink rate in healthy subjects

Paparella

Stefano

Fasolino

et al. 2020

Sci Rep

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“…For example, a driver may hold his/her chest when he/she has heart pain, or the driver hangs one hand when he/she has hemiplegia. Sleepiness state will make a driver close his/her eyes for a long time, increase the blink period, and slow heart rate (bradycardia) [27], so 'eyes closed' and 'bradycardia' can be cue signs of sleepiness. These cue signs could be an indicator to estimate symptoms, so a monitoring system is required to detect these cue signs.…”

Section: A Required Workmentioning

confidence: 99%

Toward Health–Related Accident Prevention: Symptom Detection and Intervention Based on Driver Monitoring and Verbal Interaction

Hayashi

Kamezaki

Sugano

2021

IEEE Open J. Intell. Transp. Syst.

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Professional drivers are required to safely transport passengers and/or properties of customers to their destinations, so they must keep being mentally and physically healthy. Health problems will largely affect driving performance and sometimes cause loss of consciousness, which results in injury, death, and heavy compensation. Conventional systems can detect the loss of consciousness or urgently stop the vehicle to prevent accidents, but detection of symptoms of diseases and providing support before the driver loses consciousness is more reasonable. It is challenging to earlier detect symptoms with high confidence. Toward solving these problems, we propose a new method with a multi-sensor based driver monitoring system to detect cues of symptoms quickly and a verbal interaction system to confirm the internal state of the driver based on the monitoring results to reduce false positives. There is almost no data that records abnormal conditions while driving and tests with unhealthy participants are dangerous and ethically unacceptable, so we developed a system with pseudo-symptom data and did outlier detection only with normal driving data. From data collection experiments, we defined the confidence level derived from cue signs. The results of evaluation experiments showed that the proposed system worked well in pseudo headache and drowsiness detection scenarios. We found that signs of drowsiness varied with individual drivers, so the multi-sensor based driver monitoring system was proved to be effective. Moreover, we found that there were individual differences in how the cue signs appeared, so we can propose an online re-training method to make the system adapt to individual drivers.

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“…The concerning economic burden is considerable, additionally, these accidents may lead to serious injuries and death. In order to reduce the economic and health impact of drowsiness, accurate and simple models are desired for objectively measuring drowsiness [11]. Numerous physiological, subjective, and behavioral measures of driver's drowsiness have been existing, however, each has limitations.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Implementation of driver's drowsiness assistance model based on eye movements detection

Waleed

Abbas

Hasan

2020

EEJET

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Annually, lots of persons are carrying lifelong disabilities or losing their lives owing to fatal accidents on the road. In addition to mechanical failures and people errors, driver's drowsiness represents one of the fundamental reasons for fatal accidents on the road. When drivers feel drowsy, lots of physiological and behavioral symptoms appear such as changes in the waves of the human brain, changes in facial expressions, variations in eye activity, decreasing head movements, etc. Therefore, there is a significant necessity to provide developed models of driver's drowsiness detection that exploit these symptoms for reducing accidents by warning drivers of drowsiness and fatigue. This paper concentrates on proposing a driver's drowsiness assistance model to monitor and alarm drivers by utilizing a behavioral-based method (eye movements detection method). In the proposed method of detecting eye movements (closed/opened), the Advanced Local Binary Pattern (Advanced LBP) is used, in which the descriptors are utilized to represent eye images to extract the tissue features of different persons in the driving car to see if the driver is in a drowsy state or not and this occurs after recording the driver's video and detecting the eyes of the driver. To extract the features in this way, the image of the eyes is divided into small regions through the Advanced LBP and sequenced into a single feature vector, where this method is used to determine the similarity features in the training group and to classify the eye image. The Naive Bayes classifier (NB) and Support Vector Machine (SVM) are utilized for giving good accuracy. The results indicate that the system has a high accuracy rate compared with the other existing methods, where the accuracy rate of NB and SVM using an eye detection dataset with training 90 % and testing 10% is 96 % and 97 %, respectively

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Narrative review: Do spontaneous eye blink parameters provide a useful assessment of state drowsiness?

Cited by 49 publications

References 58 publications

Painful stimulation increases spontaneous blink rate in healthy subjects

Painful stimulation increases spontaneous blink rate in healthy subjects

Toward Health–Related Accident Prevention: Symptom Detection and Intervention Based on Driver Monitoring and Verbal Interaction

Implementation of driver's drowsiness assistance model based on eye movements detection

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