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

• Published in: Journal of sleep research Vol. 20; no. 3; pp. 416 - 424
• Main Authors: SHIN, DUK, SAKAI, HIROYUKI, UCHIYAMA, YUJI
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01.09.2011
• Subjects: Accidents, Traffic - prevention & control; Accidents, Traffic - psychology; Adult; Algorithms; Automobile Driving - psychology; driving simulation; drowsy driving; Electrooculography; Eye Movement Measurements; Eye Movements - physiology; Female; Humans; Male; SEM detection algorithm; Sleep Deprivation - physiopathology; Sleep Deprivation - psychology; sleep‐related accidents; slow eye movement
• ISSN: 0962-1105; 1365-2869
• DOI: 10.1111/j.1365-2869.2010.00891.x

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−1. 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 Scheffé’s 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.