Drowsiness Detection Based on Eyelid Movement

Liu, Danghui; Sun, Peng; Xiao, Yanqing; Yin, Yunfei

doi:10.1109/etcs.2010.292

Cited by 44 publications

(9 citation statements)

References 9 publications

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“…Currently, there are various proprietary and open systems that can be used and/or adapted to the surgical scenario in order to estimate vigilance state (76) and workload metrics (77, 78). Other physiological measures of drowsiness have been repeatedly shown to be useful for predicting drowsiness in specific context, such as (i) PERCLOSE (79, 80), (ii) blinking rate, (iii) eyelid movements (81), (iv) head nodding, (v) leap stretch (82, 83), and so forth slow eye movements were shown to correlate with EEG theta and delta power band as well as with the nadir tympanic temperature and subjective sleepiness (84). Although these relations were proven to be robust only in a closed eye condition, hence limiting severely the usefulness of this metric in the field.…”

Section: Objective and Longitudinal Tracking Of Sleep And Drowsinessmentioning

confidence: 99%

Effects of Sleep Deprivation on Surgeons Dexterity

et al. 2019

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Sleep deprivation is an ordinary aspect in the global society and its prevalence is increasing. Chronic and acute sleep deprivation have been linked to diabetes and heart diseases as well as depression and enhanced impulsive behaviors. Surgeons are often exposed to long hour on call and few hours of sleep in the previous days. Nevertheless, few studies have focused their attention on the effects of sleep deprivation on surgeons and more specifically on the effects of sleep deprivation on surgical dexterity, often relying on virtual surgical simulators. A better understanding of the consequences of sleep loss on the key surgical skill of dexterity can shed light on the possible risks associated to a sleepy surgeon. In this paper, the authors aim to provide a comprehensive review of the relationship between sleep deprivation and surgical dexterity.

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Section: Objective and Longitudinal Tracking Of Sleep And Drowsinessmentioning

confidence: 99%

Effects of Sleep Deprivation on Surgeons Dexterity

et al. 2019

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“…Even though it enables faster detection, results depend on the data set used for training the features. *For correspondence Various approaches exist in literature for eye detection and eye state determination using distance between eyelids [34], iris detection [35] and eyelid movement [36,37]. Methods based on eyelid information do not deliver satisfactory results on dark skins and in low illuminations, and the iris methods require images of higher resolution for good results.…”

Section: Introductionmentioning

confidence: 99%

Open-eye detection using iris–sclera pattern analysis for driver drowsiness detection

Panicker

Nair

2017

Sādhanā

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This paper proposes a novel approach for open-eye detection that can be used in driver drowsiness analysis based on computer vision techniques. The proposed method captures the driver video using a lowresolution camera. The proposed drowsiness detection system has three main stages. The first stage is face detection using elliptical approximation and template matching techniques. In the second stage, the open eye is detected using the proposed iris-sclera pattern analysis method. In the third stage, the drowsiness state of the driver is determined using PERcentage of eye CLOSure (PERCLOS) measure. The entire system is designed to be independent of any specific data sets for face or eye detection. The proposed method for open-eye detection uses basic image processing concepts of morphological and laplacian operations. The proposed system was evaluated with real-life images and videos. Open-eye detection accuracy of 93% was achieved.

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“…Another symptom for driver fatigue detection that was introduced in [60] is the rate of sequential blinking. In this method, the number of sequential blinking with very close intervals is counted and then sequential eye blink rate will be used to measure fatigue.…”

Section: Eye Blink Ratementioning

confidence: 99%