A new method for assessing the risks of drowsiness while driving

Johns, Murray W.; Chapman, Robert J.; Crowley, Kate; Tucker, Andrew

doi:10.1007/s11818-008-0330-3

Cited by 51 publications

(46 citation statements)

References 10 publications

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“…The commercially available system is designed to emit auditory warnings when individuals reach a JDS score of 4.5 to 4.9 (cautionary level of sleepiness), and a score of 5.0 or above (critical level of sleepiness), which is associated with an increased risk of severe lane excursions on a driving simulator. 21,60,61 Positive Amplitude/Velocity Ratio (PosAVR)…”

Section: Johns Drowsiness Scale Score (Jds)mentioning

confidence: 99%

Ocular Measures of Sleepiness Are Increased in Night Shift Workers Undergoing a Simulated Night Shift Near the Peak Time of the 6-Sulfatoxymelatonin Rhythm

Ftouni

Nicholas

Kennaway

et al. 2015

Journal of Clinical Sleep Medicine

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Study Objectives:The study examined the relationship between the circadian rhythm of 6-sulphatoxymelatonin (aMT6s) and ocular measures of sleepiness and neurobehavioral performance in shift workers undergoing a simulated night shift. Methods: Twenty-two shift workers (mean age 33.4, SD 11.8 years) were tested at approximately the beginning (20:00) and the end (05:55) of a simulated night shift in the laboratory. At the time point corresponding to the end of the simulated shift, 14 participants were classifi ed as being within range of 6-sulphatoxymelatonin (aMT6s) acrophasedefi ned as 3 hours before or after aMT6s peak-and 8 were classifi ed as outside aMT6s acrophase range. Participants completed the Karolinska Sleepiness Scale (KSS) and the auditory psychomotor vigilance task (aPVT). Waking electroencephalography (EEG) was recorded and infrared refl ectance oculography was used to collect ocular measures of sleepiness: positive and negative amplitude/velocity ratio (PosAVR, NegAVR), mean blink total duration (BTD), the percentage of eye closure (%TEC), and a composite score of sleepiness levels (Johns Drowsiness Scale; JDS).Results: Participants who were tested within aMT6s acrophase range displayed higher levels of sleepiness on ocular measures (%TEC, BTD, PosAVR, JDS), objective sleepiness (EEG delta power frequency band), subjective ratings of sleepiness, and neurobehavioral performance, compared to those who were outside aMT6s acrophase range. Conclusions:The study demonstrated that objective ocular measures of sleepiness are sensitive to circadian rhythm misalignment in shift workers. Keywords: oculometrics, circadian misalignment, shift work, performance, sleepiness Citation: Ftouni S, Sletten TL, Nicholas CL, Kennaway DJ, Lockley SW, Rajaratnam SM. Ocular measures of sleepiness are increased in night shift workers undergoing a simulated night shift near the peak time of the 6-sulfatoxymelatonin rhythm. J Clin Sleep Med 2015;11(10):1131 -1141 .pii: jc-00268-14 http://dx.doi.org/10.5664/jcsm.5086 S hift work has become a ubiquitous part of labor practices due to the increasing pressure for 24-hour provision of goods and services. This drive for round-the-clock productivity, however, has resulted in negative consequences to health, safety, and performance.1 Such consequences have been welldocumented in a number of occupational sectors in which peak functioning during the night shift is critical, for example, commercial transport, mining, emergency services, and health care. The impairment to alertness that occurs during night shift work is mainly attributed to misalignment of the imposed sleep-wake cycle from the internally generated circadian (~24-hour) rhythm of sleepiness, 3 often against a background of chronic sleep defi ciency and prolonged periods of wakefulness. Alertness and neurobehavioral performance peak when the circadian pacemaker promotes wakefulness during the daytime, and decline markedly during the night, particularly between 03:00 and 06:00, 4 as the circadian pacemaker strongly promotes...

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Section: Johns Drowsiness Scale Score (Jds)mentioning

confidence: 99%

Ocular Measures of Sleepiness Are Increased in Night Shift Workers Undergoing a Simulated Night Shift Near the Peak Time of the 6-Sulfatoxymelatonin Rhythm

Ftouni

Nicholas

Kennaway

et al. 2015

Journal of Clinical Sleep Medicine

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“…Some authors distinguish fatigue from drowsiness as the former does not f uctuate rapidly, over periods of a few seconds, as drowsiness. Usually, rest and inactivity relieves fatigue, however, it makes drowsiness worse [7].…”

mentioning

confidence: 99%

Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm

Khushaba

Kodagoda

Lal

et al. 2011

IEEE Trans. Biomed. Eng.

439

223

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Abstract-Driver drowsiness and loss of vigilance are a major cause of road accidents. Monitoring physiological signals while driving provides the possibility of detecting and warning of drowsiness and fatigue. The aim of this paper is to maximize the amount of drowsiness-related information extracted from a set of Electroencephalogram (EEG), Electrooculogram (EOG), and Electrocardiogram (ECG) signals during a simulation driving test. Specifically, we develop an efficient fuzzy mutual information based wavelet packet transform (FMIWPT) feature extraction method for classifying the driver drowsiness state into one of predefined drowsiness levels. The proposed method estimates the required mutual information using a novel approach based on fuzzy memberships providing an accurate information content estimation measure. The quality of the extracted features was assessed on datasets collected from thirty-one drivers on a simulation test. The experimental results proved the significance of FMIWPT in extracting features that highly correlate with the different drowsiness levels achieving a classification accuracy of 95%-97% on average across all subjects.

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“…The lack of validated real-time objective indicators of drowsiness for fi eld research and operational settings, such as driving, restricts both the ability to accurately assess drowsiness in these settings and the development of fi eld-based interventions for drowsiness. Initial laboratory studies have suggested that some ocular measures may be good indicators of drowsiness [3][4][5] ; however, there is a paucity of detailed evaluation of the utility of different ocular measures for this purpose.Electroencephalography (EEG) is the gold standard method for quantifying sleep state (awake versus sleep). Although EEG changes occur with drowsiness in the wake state (increased …”

mentioning

confidence: 99%

The Accuracy of Eyelid Movement Parameters for Drowsiness Detection

Wilkinson

Jackson

Westlake

et al. 2013

Journal of Clinical Sleep Medicine

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alpha and theta activity), 6 they are diffi cult to measure in fi eld settings due to signal artifact, are not readily amenable to realtime signal processing and are not highly predictive of impaired behavior due to drowsiness. 7 These features have hindered the use of EEG for assessing drowsiness in fi eld settings and the potential to use it to provide real-time drowsiness monitoring.Changes in the frequency, amplitude, and duration of blinks and episodes of slow eye closure occur in response to increased drowsiness caused by sleep deprivation and circadian rhythm effects. [8][9][10][11] While blink duration in rested conditions lasts for less S C I E N T I F I C I N V E S T I G A T I O N SD rowsiness as a result of sleep deprivation, circadian effects, or sleep disorders is a major risk factor for motor vehicle and occupational accidents.1,2 Objective indicators of drowsiness may allow sleepy individuals to be identifi ed in real time in the laboratory, occupational settings, and on the road, with the potential to intervene and prevent accidents. Ideally, indicators of drowsiness for this purpose should be able to detect brief periods of inattention, which may result in an individual failing to respond to hazards in the environment. The lack of validated real-time objective indicators of drowsiness for fi eld research and operational settings, such as driving, restricts both the ability to accurately assess drowsiness in these settings and the development of fi eld-based interventions for drowsiness. Initial laboratory studies have suggested that some ocular measures may be good indicators of drowsiness [3][4][5] ; however, there is a paucity of detailed evaluation of the utility of different ocular measures for this purpose.Electroencephalography (EEG) is the gold standard method for quantifying sleep state (awake versus sleep). Although EEG changes occur with drowsiness in the wake state (increased BRIEF SUMMARYCurrent Knowledge/Study Rationale: Objective indicators of drowsiness have the potential to identify drowsy drivers. This study evaluated the accuracy of ocular measures for detecting drowsiness-related behavioral impairment in real-time. Study Impact: Several ocular measures, particularly those measuring the average duration of episodes of eye closure, were found to be accurate in the real-time detection of behavioral impairment in the laboratory. Ocular measures are promising indicators of real-time drowsiness.

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A new method for assessing the risks of drowsiness while driving

Cited by 51 publications

References 10 publications

Ocular Measures of Sleepiness Are Increased in Night Shift Workers Undergoing a Simulated Night Shift Near the Peak Time of the 6-Sulfatoxymelatonin Rhythm

Ocular Measures of Sleepiness Are Increased in Night Shift Workers Undergoing a Simulated Night Shift Near the Peak Time of the 6-Sulfatoxymelatonin Rhythm

Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm

The Accuracy of Eyelid Movement Parameters for Drowsiness Detection

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