Slow eye movement detection can prevent sleep-related accidents effectively in a simulated driving task

Abstract

SUMMARY A delayed response caused by sleepiness can result in severe car accidents. Previous studies suggest that slow eye movement (SEM) is a sleep-onset index related to delayed response. This study was undertaken to verify that SEM detection is effective for preventing sleep-related accidents. We propose a real-time detection algorithm of SEM based on feature-extracted parameters of electrooculogram (EOG), i.e. amplitude and mean velocity of eye movement. In Experiment 1, 12 participants (33.5 ± 7.3 years) performed an auditory detection task with EOG measurement to determine the threshold parameters of the proposed algorithm. Consequently, the valid threshold parameters were determined, respectively, as >5°and <30°s. In Experiment 2, 11 participants (32.8 ± 7.2 years) performed a simulated car-following task to verify that the SEM detection is effective for preventing sleep-related accidents. Accidents in the SEM condition were significantly more numerous than in the non-SEM condition (P < 0.01, one-way repeated-measures anova followed by Scheffe´Õs test). Furthermore, no accident occurred in the SEM condition with a warning generated using the proposed algorithm. Results also demonstrate clearly that the SEM detection can prevent sleep-related accidents effectively in this simulated driving task.

Detecting deteriorated vigilance using percentage of eyelid closure time during behavioral maintenance of wakefulness tests

“…In addition, Åkersted and Gillberg (1990) demonstrated that the changes of SEM with eyes open did not occur until the subjective perceptions of sleepiness fell between "sleepy" and "extremely sleepy-fighting sleep" (KSS≥ 7). SEM detection is apparently an effective method for preventing sleepiness-related accidents by falling asleep, as shown in a study by Shin et al (2011).…”

“…Because ocular variables (e.g., pupils, eye movement, and blinking) change depending on the level of sleepiness and because they can be measured without contact or restraint, several researchers have investigated methods for measuring drivers' sleepiness using ocular variables (Åkerstedt et al, 2010;Dinges et al, 1998;Johns et al, 2007;Shin et al, 2011;Ueno et al, 2007). Studies using a sleep deprivation protocol have demonstrated that participants show a decrease in the peak velocity of saccadic eye movement (De Gennaro et al, 2000) and relative peak velocity of closing or reopening of blink , an increase of the percentage of time in each minute occupied by slow eye movement (SEM) (Åkerstedt and Gillberg, 1990) and by eyelid closure (percentage of eyelid closure time over the pupil per certain period: PERCLOS) (Dinges et al, 1998;Wierwille and Ellsworth, 1994), increase (Barbato et al, 2007) or decrease (De Gennaro et al, 2005) of blink frequency, prolonged eyelid closure time during blink and total blink duration , and smaller pupil size (Wilhelm et al, 1998) after or during sleep deprivation.…”

“…Studies using a sleep deprivation protocol have demonstrated that participants show a decrease in the peak velocity of saccadic eye movement (De Gennaro et al, 2000) and relative peak velocity of closing or reopening of blink , an increase of the percentage of time in each minute occupied by slow eye movement (SEM) (Åkerstedt and Gillberg, 1990) and by eyelid closure (percentage of eyelid closure time over the pupil per certain period: PERCLOS) (Dinges et al, 1998;Wierwille and Ellsworth, 1994), increase (Barbato et al, 2007) or decrease (De Gennaro et al, 2005) of blink frequency, prolonged eyelid closure time during blink and total blink duration , and smaller pupil size (Wilhelm et al, 1998) after or during sleep deprivation. The relation between ocular variables and driving performance has also been investigated (Åkerstedt et al, 2010;Johns et al, 2007;Shin et al, 2011). Åkerstedt and a colleague reported that the mean blink duration of participants while performing a driving simulator task was prolonged after working a night shift (Åkerstedt et al, 2005;Anund et al, 2009).…”

“…Johns et al (2007) also demonstrated that their drowsiness scale, which is created based on a combination of several ocular variables including saccades and blink, can predict the deviations from the road centerline while performing a driving simulator task. Recently, Shin et al (2011) demonstrated that rear-end collisions during a simulated car-following task occurred mainly after the occurrence of SEM.…”

Application of eye-tracking in drivers testing: A review of research

“…If a target is moving quickly across the entire field of vision, pursuit is often accompanied by a saccade to catch up with the target. Some authors claim that it is desirable to analyze slow eye movements to evaluate the state of a driver's activity [15]. Vergence eye movements occur when the eyes are guided to the centre of the field of view to focus on a near object.…”