On-Line Detection of Drowsiness Using Brain and Visual Information

Picot, Antoine; Charbonnier, Sylvie; Caplier, Alice

doi:10.1109/tsmca.2011.2164242

Cited by 119 publications

(73 citation statements)

References 22 publications

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“…These studies showed the possibility of detection of vigilance [35], fatigue [42,43] and drowsiness [41,44,45]. Mostly, they considered the neural correlates necessary for detection of drowsiness [35,[40][41][42][43][44][45][46]. However, to avoid false alerts, the characteristics of the hemodynamic response due to drowsiness, along with the neuronal response, should also be studied.…”

Section: Introductionmentioning

confidence: 99%

Passive BCI based on drowsiness detection: an fNIRS study

Khan

Hong

2015

Biomed. Opt. Express

183

136

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Abstract:We use functional near-infrared spectroscopy (fNIRS) to discriminate the alert and drowsy states for a passive brain-computer interface (BCI). The passive brain signals for the drowsy state are acquired from the prefrontal and dorsolateral prefrontal cortex. The experiment is performed on 13 healthy subjects using a driving simulator, and their brain activity is recorded using a continuous-wave fNIRS system. Linear discriminant analysis (LDA) is employed for training and testing, using the data from the prefrontal, left-and right-dorsolateral prefrontal regions. For classification, eight features are tested: mean oxyhemoglobin, mean deoxyhemoglobin, skewness, kurtosis, signal slope, number of peaks, sum of peaks, and signal peak, in 0~5, 0~10, and 0~15 second time windows, respectively. The results show that the best performance for classification is achieved using mean oxyhemoglobin, the signal peak, and the sum of peaks as features. The average accuracies in the right dorsolateral prefrontal cortex (83.1, 83.4 and 84.9% in the 0~5, 0~10 and 0~15 second time windows, respectively) show that the proposed method has an effective utility for detection of drowsiness for a passive BCI.

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Section: Introductionmentioning

confidence: 99%

Passive BCI based on drowsiness detection: an fNIRS study

Khan

Hong

2015

Biomed. Opt. Express

183

136

View full text Add to dashboard Cite

show abstract

“…Fatigue detection method based on brain and visual activity was proposed by [5]. Another method assessed fatigue by using electrocardiography, eye movement [6]. A pulse wave sensor was used to detect lethargy [7].…”

Section: Related Workmentioning

confidence: 99%

Hypovigilant System: An Approach for Lethargy Detection

Bhor¹,

Mahajan²,

Dharmadhikari³

et al. 2016

IJCA

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There is much attentional impairment while driving that affect driver's reaction. Among which driving while lethargic is one of the major causes behind road accidents, and exposes the driver to a large extent of crash risk compared to driving while alert. Therefore, the use of an assistive system that monitor a driver's level of vigilance and alert the driver in case of lethargy can be significant in the prevention of accidents. This paper introduces a new approach towards detection of driver's lethargy based on yawning measurement and head movement. This involves several steps which includes the real time detection and tracking of head movement, the detection of yawning based on measuring the amount of changes in the mouth contour area. Test results express that the proposed system is able to measure the aforesaid parameters and detect driver's lethargy.

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“…Fatigue detection method based on brain and visual activity was proposed by [5]. Another method assessed fatigue by using electrocardiography, eye movement [6].…”

Section: Related Workmentioning

confidence: 99%

Survey on Driver’s Drowsiness Detection System

Dharmadhikari¹,

Bhor²,

Mahajan³

et al. 2015

IJCA

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There is much attentional impairment while driving that affect driver's reaction. Among which driving while drowsy is one of the major causes behind road accidents, and exposes the driver to a much higher crash risk compared to driving while alert. Therefore, the use of an assistive system that monitor a driver's level of vigilance and alert the driver in case of drowsiness can be significant in the prevention of accidents. This paper introduces a new approach towards detection of driver's drowsiness based on yawning measurement and head movement. This involves several steps including the real time detection and tracking of driver's face, detection and tracking of the mouth contour, the detection of yawning based on measuring both the rate and the amount of changes in the mouth contour area and head movement tracking. Test results demonstrate that the proposed system can efficiently measure the aforementioned parameters and detect driver's drowsiness.

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On-Line Detection of Drowsiness Using Brain and Visual Information

Cited by 119 publications

References 22 publications

Passive BCI based on drowsiness detection: an fNIRS study

Passive BCI based on drowsiness detection: an fNIRS study

Hypovigilant System: An Approach for Lethargy Detection

Survey on Driver’s Drowsiness Detection System

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