Intelligent Driver Monitoring Systems Based on Physiological Sensor Signals: A Review

被引:0
|
作者
Begum, Shahina [1 ]
机构
[1] Malardalen Univ, Innovat Design & Engn Dept, SE-72123 Vasteras, Sweden
关键词
MENTAL WORKLOAD; SLEEPINESS; CAR; EEG; DROWSINESS; STATE; CLASSIFICATION; EVALUATE; FATIGUE; STRESS;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Drowsiness, stress and lack of concentration caused by a variety of different factors is a serious problem in traffic. Many traffic accidents are due to these risky behaviors of the drivers. A system which recognizes the state of the driver and e.g. suggests breaks when stress level is too high or driver is too tired would enable large savings and reduces accident. Today different physiological sensor signals such as Electrocardiogram (ECG), Elektro-okulogram (EOG), Electroencephalogram (EEG) and Pulse Oximeter (Oxygen saturation measurements) enable clinician to determine psychological and behavioral state with high accuracy. There are researchers working on developing intelligent systems help to monitor potential risky behaviors of drivers using sensor signals. Thus, this paper provides an overview and analysis of driver monitoring/alerting systems developments and implementations which are based on physiological sensor signals. Summarizing published research works and systems this review provides a resource for researchers, scholars and developers working in the area.
引用
收藏
页码:282 / 289
页数:8
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