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迈向对疲劳驾驶员更精准的微睡眠预测:探索人格特质和智商的作用

Towards better microsleep predictions in fatigued drivers: exploring benefits of personality traits and IQ.

作者信息

Hidalgo-Gadea Guillermo, Kreuder Annika, Krajewski Jarek, Vorstius Christian

机构信息

Department of General and Biological Psychology, University of Wuppertal, Wuppertal, Germany.

Institute for Experimental Psychology, University of Düsseldorf, Düsseldorf, Germany.

出版信息

Ergonomics. 2021 Jun;64(6):778-792. doi: 10.1080/00140139.2021.1882707. Epub 2021 Apr 16.

Abstract

Fatigued driving is one of the main contributors to road traffic accidents. Poor sleep quality and lack of sleep negatively affect driving performance, and extreme states of fatigue can cause microsleep (i.e., short episodes of sleep with complete loss of awareness). Driver monitoring systems analyse biosignals (e.g., gaze, blinking, heart rate) and vehicle data (e.g., steering wheel movements, lane holding, acceleration) to detect states of fatigue and prevent accidents. We argue that inter-individual differences in personality, sensation seeking behaviour, and intelligence could improve microsleep prediction, in addition to sleepiness. We tested 144 male participants in a supervised driving track after 27 hours of sleep deprivation. More than 74% of drivers experienced microsleep, after an average driving time of 52 min. Overall, prediction models for microsleep vulnerability and driving time before microsleep were significantly improved by conscientiousness, sensation seeking and non-verbal IQ, in addition to situational sleepiness, as individual risk factors. : This study offers valuable insights for the design of driver monitoring systems. The use of individual risk factors such as conscientiousness, sensation seeking, and non-verbal IQ can increase microsleep prediction. These findings may improve monitoring systems based solely on physiological signals (e.g., blinking, heart rate) and vehicle data (e.g., steering wheel movements, acceleration, cornering). ADAC: Allgemeiner Deutscher Automobil Club; ANOVA: analysis of variance; AIC: Akaike information criteria; CI: confidence interval; GPS: global positioning system; IQ: intelligence quotient; IQR: inter quartile range; KSS: Karolinska sleepiness scale; NEO-PI-R: revised NEO personality inventory; OLS: ordinary least squares; PSQI: Pittsburgh sleep quality index; SPM: standard progressive matrices; SSS: sensation seeking scale; WHO: World Health Organization.

摘要

疲劳驾驶是道路交通事故的主要原因之一。睡眠质量差和睡眠不足会对驾驶性能产生负面影响,极度疲劳状态会导致微睡眠(即短暂的睡眠发作,完全失去意识)。驾驶员监测系统分析生物信号(如注视、眨眼、心率)和车辆数据(如方向盘运动、车道保持、加速度),以检测疲劳状态并预防事故。我们认为,除了困倦之外,个体在性格、寻求刺激行为和智力方面的差异也可以改善微睡眠预测。我们在144名男性参与者睡眠剥夺27小时后,在一个受监督的驾驶赛道上对他们进行了测试。平均驾驶52分钟后,超过74%的驾驶员经历了微睡眠。总体而言,除了情境性困倦作为个体风险因素外,尽责性、寻求刺激和非言语智商显著改善了微睡眠易感性和微睡眠前驾驶时间的预测模型。本研究为驾驶员监测系统的设计提供了有价值的见解。使用尽责性、寻求刺激和非言语智商等个体风险因素可以提高微睡眠预测。这些发现可能会改进仅基于生理信号(如眨眼、心率)和车辆数据(如方向盘运动、加速度、转弯)的监测系统。ADAC:德国汽车俱乐部;ANOVA:方差分析;AIC:赤池信息准则;CI:置信区间;GPS:全球定位系统;IQ:智商;IQR:四分位间距;KSS:卡罗林斯卡嗜睡量表;NEO-PI-R:修订的大五人格量表;OLS:普通最小二乘法;PSQI:匹兹堡睡眠质量指数;SPM:标准渐进矩阵;SSS:寻求刺激量表;WHO:世界卫生组织。

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