基于PERCLOS的睡意检测技术:当前证据和未来方向。

Takashi Abe
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引用次数: 0

摘要

与睡眠不足和昼夜节律失调相关的嗜睡是事故和人为失误的风险因素。眼睛闭合80%以上的时间百分比(PERCLOS)是用于被动检测睡意的最有效指标之一,睡意随着睡眠不足、部分睡眠限制后、夜间以及警惕性测试、模拟驾驶和道路驾驶期间的其他睡意操作而增加。然而,已经报道了一些案例,其中PERCLOS不受嗜睡操作的影响,例如在中度嗜睡条件下、老年人和航空相关任务中。此外,尽管PERCLOS是在精神运动警惕性测试或行为保持清醒测试中检测与嗜睡相关的表现障碍的最敏感指标之一,但目前还没有一个指标可以作为检测驾驶或其他现实情况下嗜睡的最佳指标。基于目前已发表的证据,这篇叙述性综述建议未来的研究应侧重于:(1)标准化,以最大限度地减少研究之间PERCLOS定义的差异;(2) 使用基于PERCLOS技术的单个设备进行广泛验证;(3) 开发和验证将PERCLOS与其他行为和/或生理指标相结合的技术,因为单独的PERCLOS可能对检测入睡以外的因素(如注意力不集中或分心)引起的嗜睡不够敏感;以及(4)针对睡眠障碍的进一步验证研究和现场试验以及在现实世界环境中的试验。通过这些研究,基于PERCLOS的技术可能有助于预防与嗜睡相关的事故和人为失误。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PERCLOS-based technologies for detecting drowsiness: current evidence and future directions.

Drowsiness associated with sleep loss and circadian misalignment is a risk factor for accidents and human error. The percentage of time that the eyes are more than 80% closed (PERCLOS) is one of the most validated indices used for the passive detection of drowsiness, which is increased with sleep deprivation, after partial sleep restriction, at nighttime, and by other drowsiness manipulations during vigilance tests, simulated driving, and on-road driving. However, some cases have been reported wherein PERCLOS was not affected by drowsiness manipulations, such as in moderate drowsiness conditions, in older adults, and during aviation-related tasks. Additionally, although PERCLOS is one of the most sensitive indices for detecting drowsiness-related performance impairments during the psychomotor vigilance test or behavioral maintenance of wakefulness test, no single index is currently available as an optimal marker for detecting drowsiness during driving or other real-world situations. Based on the current published evidence, this narrative review suggests that future studies should focus on: (1) standardization to minimize differences in the definition of PERCLOS between studies; (2) extensive validation using a single device that utilizes PERCLOS-based technology; (3) development and validation of technologies that integrate PERCLOS with other behavioral and/or physiological indices, because PERCLOS alone may not be sufficiently sensitive for detecting drowsiness caused by factors other than falling asleep, such as inattention or distraction; and (4) further validation studies and field trials targeting sleep disorders and trials in real-world environments. Through such studies, PERCLOS-based technology may contribute to preventing drowsiness-related accidents and human error.

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