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首页> 外文期刊>Journal of sleep research >Slow eye movement detection can prevent sleep-related accidents effectively in a simulated driving task.
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Slow eye movement detection can prevent sleep-related accidents effectively in a simulated driving task.

机译：慢速眼动检测可以在模拟驾驶任务中有效防止与睡眠有关的事故。

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摘要

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 degrees and <30 degrees 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 Scheffe'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.
机译：嗜睡引起的反应迟钝可能导致严重的车祸。先前的研究表明，慢眼动（SEM）是与延迟反应相关的睡眠发作指数。进行这项研究是为了证明SEM检测对于预防与睡眠有关的事故是有效的。我们提出了一种基于眼电图（EOG）特征提取参数（即眼动幅度和平均速度）的SEM实时检测算法。在实验1中，有12位参与者（33.5 +/- 7.3岁）执行了带有EOG测量的听觉检测任务，以确定所提出算法的阈值参数。因此，有效阈值参数分别确定为> 5度和<30度s（-1）。在实验2中，有11位参与者（32.8 +/- 7.2岁）执行了模拟的跟车任务，以验证SEM检测对于预防与睡眠有关的事故有效。 SEM条件下的事故比非SEM条件下的事故要多得多（P <0.01，单向重复测量方差分析，然后进行Scheffe检验）。此外，使用所提出的算法生成的警告在SEM条件下没有发生意外。结果还清楚地表明，在此模拟驾驶任务中，SEM检测可以有效地防止与睡眠有关的事故。

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