Abstract
The rapid development of driver fatigue detection technology indicates important significance of traffic safety. The authors’ main goals of this Letter are principally three: (i) A middleware architecture, defined as process unit (PU), which can communicate with personal electroencephalography (EEG) node (PEN) and cloud server (CS). The PU receives EEG signals from PEN, recognises the fatigue state of the driver, and transfer this information to CS. The CS sends notification messages to the surrounding vehicles. (ii) An android application for fatigue detection is built. The application can be used for the driver to detect the state of his/her fatigue based on EEG signals, and warn neighbourhood vehicles. (iii) The detection algorithm for driver fatigue is applied based on fuzzy entropy. The idea of 10-fold cross-validation and support vector machine are used for classified calculation. Experimental results show that the average accurate rate of detecting driver fatigue is about 95%, which implying that the algorithm is validity in detecting state of driver fatigue.
Highlights
Driver fatigue is receiving more and more attention in the traffic safety field, because it affects the driver’s ability to make decision, slow down reaction time, decrease driver’s attention, and contributes for increasing the number of accidents [1]
We developed and evaluated a mobile driver fatigue detection network based on EEG signals
The EEG signals transmitted by the personal electroencephalography (EEG) node (PEN) are processed in the process unit (PU) to detect the driver’s fatigue state
Summary
Driver fatigue is receiving more and more attention in the traffic safety field, because it affects the driver’s ability to make decision, slow down reaction time, decrease driver’s attention, and contributes for increasing the number of accidents [1]. If driver fatigue can be detected, drivers will get useful information about their fatigue and so decrease the traffic accident [5]. Numerous experiments show physiological signals can be applied to detect fatigue state [8], including electroencephalograph (EEG), electrooculography (EOG), electrocardiogram (ECG) and electromyogram (EMG). Drivers have reduced levels of alertness when they are fatigued. This is accompanied by some consistently measurable changes in the EEG signals. We developed and evaluated a mobile driver fatigue detection network based on EEG signals
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