Evaluation of Eye Metrics as a Detector of Fatigue

McKinley, R. Andy; McIntire, Lindsey K.; Schmidt, Regina; Repperger, D.W.; Caldwell, John A.

doi:10.1177/0018720811411297

Cited by 70 publications

(50 citation statements)

References 7 publications

(11 reference statements)

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“…Most of the published studies on using behavioral approaches to determine drowsiness, focus on blinking [43–45]. PERCLOS (which is the percentage of eyelid closure over the pupil over time, reflecting slow eyelid closures, or “droops”, rather than blinks) has been analyzed in many studies [8,46–48]. This measurement has been found to be a reliable measure to predict drowsiness [46] and has been used in commercial products such as Seeing Machines [49] and Lexus [50].…”

Section: Methods For Measuring Drowsinessmentioning

confidence: 99%

Detecting Driver Drowsiness Based on Sensors: A Review

Sahayadhas

Sundaraj

Murugappan

2012

Sensors

599

363

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In recent years, driver drowsiness has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. Statistics indicate the need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens. Researchers have attempted to determine driver drowsiness using the following measures: (1) vehicle-based measures; (2) behavioral measures and (3) physiological measures. A detailed review on these measures will provide insight on the present systems, issues associated with them and the enhancements that need to be done to make a robust system. In this paper, we review these three measures as to the sensors used and discuss the advantages and limitations of each. The various ways through which drowsiness has been experimentally manipulated is also discussed. We conclude that by designing a hybrid drowsiness detection system that combines non-intusive physiological measures with other measures one would accurately determine the drowsiness level of a driver. A number of road accidents might then be avoided if an alert is sent to a driver that is deemed drowsy.

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Section: Methods For Measuring Drowsinessmentioning

confidence: 99%

Detecting Driver Drowsiness Based on Sensors: A Review

Sahayadhas

Sundaraj

Murugappan

2012

Sensors

599

363

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“…However, pupil diameter and eyelid opening could indicate the current level of mental and physical workload. Once fatigue was induced by the accumulated workload, pupil diameter and eyelid opening would change and become smaller as deeper fatigue [14,15]. Also, heart rate, respiration rate and electrodermal activity also reduced as the psychophysical fatigue slowly appeared [16].…”

Section: Discussionmentioning

confidence: 99%

Pilot's visual attention allocation modeling under fatigue

Wu¹,

Wanyan²,

Zhuang

2015

THC

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Abstract. BACKGROUND: Human factors involved with visual attention mechanism and fatigue are critical causes of modern aviation accidents. OBJECTIVE: To investigate the connection between attention and flight fatigue, a mathematical model of pilot's visual attention allocation was established based on information processing channels. Multi-task condition and current psychophysical state were taken into account as well. METHODS: Sixteen participants were recruited to perform a long-term dual-task in a Boeing 737-800 flight simulator. The primary task was an envelope flight task and the secondary was an unusual attitude (UA) recovery task. Reaction time of the secondary task was recorded as a behavior performance index, while heart rate and respiration rate were measured as physiological indices as well as fixation distribution as attention allocation index. RESULTS:The experiment results showed a significant affect of experiment time that indicated the occurrence and influence of fatigue. Eye movement tracking also revealed good agreement with the predictable model and hence verified its effectiveness. Moreover, applicability of the model was validated under flight fatigue and multiple tasks condition. CONCLUSION: The current study provided a quantitative connection between pilot's visual attention allocation and flight fatigue, which was verified in the ergonomics experiment.

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“…Numerous studies have successfully used eye-tracking to detect different degraded cognitive states such as spatial disorientation [52], fatigue [53,54], attentional tunneling [55], or automation surprise [26]. Except for the adverse cognitive states, eye-tracking can also be used to infer one's intentions.…”

Section: Stage Iii: Gaze-based Flight Deck Adaptationmentioning

confidence: 99%

The Neuroergonomics of Aircraft Cockpits: The Four Stages of Eye-Tracking Integration to Enhance Flight Safety

Peysakhovich

Lefrancois

Dehais

et al. 2018

Safety

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Abstract:Commercial aviation is currently one of the safest modes of transportation; however, human error is still one major contributing cause of aeronautical accidents and incidents. One promising avenue to further enhance flight safety is Neuroergonomics, an approach at the intersection of neuroscience, cognitive engineering and human factors, which aims to create better human-system interaction. Eye-tracking technology allows users to "monitor the monitoring" by providing insights into both pilots' attentional distribution and underlying decisional processes. In this position paper, we identify and define a framework of four stages of step-by-step integration of eye-tracking systems in modern cockpits. Stage I concerns Pilot Training and Flight Performance Analysis on-ground; stage II proposes On-board Gaze Recordings as extra data for the "black box" recorders; stage III describes Gaze-Based Flight Deck Adaptation including warning and alerting systems, and, eventually, stage IV prophesies Gaze-Based Aircraft Adaptation including authority taking by the aircraft. We illustrate the potential of these four steps with a description of incidents or accidents that we could certainly have avoided thanks to eye-tracking. Estimated milestones for the integration of each stage are also proposed together with a list of some implementation limitations. We believe that the research institutions and industrial actors of the domain will all benefit from the integration of the framework of the eye-tracking systems into cockpits.

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Evaluation of Eye Metrics as a Detector of Fatigue

Abstract: Military and commercial operators could benefit from an alertness monitoring device.

Cited by 70 publications

References 7 publications

Detecting Driver Drowsiness Based on Sensors: A Review

Detecting Driver Drowsiness Based on Sensors: A Review

Pilot's visual attention allocation modeling under fatigue

The Neuroergonomics of Aircraft Cockpits: The Four Stages of Eye-Tracking Integration to Enhance Flight Safety

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