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

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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.
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Document Type: Research Article

Affiliations: 1: Tokyo Institute of Technology, Yokohama 2: Toyota Central R&D Labs, Nagakute, Aichi, Japan

Publication date: 2011-09-01

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