Laboratory of Emotion and Mental Health, Chongqing University of Arts and Sciences, Yongchuan, Chongqing, 402160, China; Department of Psychology, Wichita State University, Wichita, KS 67206, USA.
Department of Psychology, Wichita State University, Wichita, KS 67206, USA.
Appl Ergon. 2017 Nov;65:473-480. doi: 10.1016/j.apergo.2017.02.016. Epub 2017 Apr 15.
Drowsiness is one of the major factors that cause crashes in the transportation industry. Drowsiness detection systems can alert drowsy operators and potentially reduce the risk of crashes. In this study, a Google-Glass-based drowsiness detection system was developed and validated.
The proximity sensor of Google Glass was used to monitor eye blink frequency. A simulated driving study was carried out to validate the system. Driving performance and eye blinks were compared between the two states of alertness and drowsiness while driving.
Drowsy drivers increased frequency of eye blinks, produced longer braking response time and increased lane deviation, compared to when they were alert. A threshold algorithm for proximity sensor can reliably detect eye blinks and proved the feasibility of using Google Glass to detect operator drowsiness.
This technology provides a new platform to detect operator drowsiness and has the potential to reduce drowsiness-related crashes in driving and aviation.
困倦是导致交通运输行业事故的主要因素之一。瞌睡检测系统可以提醒困倦的驾驶员，从而有可能降低事故风险。本研究开发并验证了一种基于谷歌眼镜的瞌睡检测系统。
利用谷歌眼镜的接近传感器监测眨眼频率。进行了模拟驾驶研究以验证该系统。在驾驶过程中，比较了警觉和困倦两种状态下的驾驶性能和眨眼情况。
与警觉时相比，困倦的驾驶员眨眼频率增加，制动反应时间延长，车道偏离增加。接近传感器的阈值算法可以可靠地检测眨眼情况，证明了使用谷歌眼镜检测驾驶员困倦的可行性。
这项技术为检测驾驶员困倦提供了一个新平台，有可能减少驾驶和航空领域与困倦相关的事故。
