Driver Alertness Monitoring Using Fusion of Facial Features and Bio-Signals
Driver Alertness Monitoring Using Fusion of Facial Features and Bio-Signals
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DOI：
10.1109/jsen.2012.2190505
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发表时间：
2012-07-01
影响因子：
4.3
通讯作者：
Chung, Wan-Young
中科院分区：
文献类型：
--
作者：
Lee, Boon-Giin;Chung, Wan-Young
驾驶员昏昏欲睡是世界各地发生的交通事故的主要原因之一。本文描述了一种利用眼动监测和生物信号处理两种不同的方法，通过分析与疲劳有关的信息来监测驾驶员安全的方法。在基于Android的智能手机上设计了一个监控系统，通过无线传感器网络接收传感器数据，并对数据进行进一步处理，以指示驾驶员当前的驾驶能力。为了更真实地评估驾驶员的行为，集成和同步多个传感器至关重要。所应用的传感器包括用于采集驾驶员图像的视频传感器和用于采集驾驶员光体积描记信号的生物信号传感器。采用动态贝叶斯网络框架进行驾驶疲劳评估。如果驾驶疲劳被认为达到了定义的阈值，就会发出警告警报。该系统的多方面测试证明了多种特征的实际使用，特别是与离散方法的结合，它们的融合使疲劳检测更加真实和充分。
Driver drowsiness is among the leading causal factors in traffic accidents occurring worldwide. This paper describes a method to monitor driver safety by analyzing information related to fatigue using two distinct methods: eye movement monitoring and bio-signal processing. A monitoring system is designed in Android-based smartphone where it receives sensory data via wireless sensor network and further processes the data to indicate the current driving aptitude of the driver. It is critical that several sensors are integrated and synchronized for a more realistic evaluation of the driver behavior. The sensors applied include a video sensor to capture the driver image and a bio-signal sensor to gather the driver photoplethysmograph signal. A dynamic Bayesian network framework is used for the driver fatigue evaluation. A warning alarm is sounded if driver fatigue is believed to reach a defined threshold. The manifold testing of the system demonstrates the practical use of multiple features, particularly with discrete methods, and their fusion enables a more authentic and ample fatigue detection.