A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness
نویسندگان
چکیده
منابع مشابه
A Context-Aware EEG Headset System for Early Detection of Driver Drowsiness
Driver drowsiness is a major cause of mortality in traffic accidents worldwide. Electroencephalographic (EEG) signal, which reflects the brain activities, is more directly related to drowsiness. Thus, many Brain-Machine-Interface (BMI) systems have been proposed to detect driver drowsiness. However, detecting driver drowsiness at its early stage poses a major practical hurdle when using existin...
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Driving with drowsiness is one of the main causes of traffic accidents. Driver fatigue is a significant factor in a large number of vehicle accidents. The development of technologies for detecting or preventing drowsiness at the wheel is a major challenge in the field of accident avoidance systems. Due to the hazard that drowsiness presents on the road, methods need to be developed for countera...
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Drivers who do not take regular breaks when driving long distances run a high risk of becoming drowsy a state which they often fail to recognize early enough according to the experts. Studies show that around one quarter of all serious motorway accidents are attributable to sleepy drivers in need of a rest, meaning that drowsiness causes more road accidents than drink-driving. Attention assist ...
متن کاملContext-Aware Driver Assistance System
In the last few years, significant improvements have been made in the area of vehicular communication systems. In fact, vehicle-to-vehicle communication and vehicle-to-infrastructure are considered a key concept for keeping roads safe. An efficient implementation of these systems is necessary to ensure the safety and to reduce the car collision rates. This paper proposes a Context-Aware Driver ...
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Today most accidents are caused by drivers’ fatigue, drowsiness and losing attention on the road ahead. In this paper, a system is introduced, using RGB-D cameras to automatically identify drowsiness and give warning. In this system two important modules have been utilized simultaneously to identify the state of driver’s mouth and eyes for detecting drowsiness. At first, using the depth informa...
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ژورنال
عنوان ژورنال: Sensors
سال: 2015
ISSN: 1424-8220
DOI: 10.3390/s150820873