Abstract
Driver fatigue is a leading factor in road accidents that can cause serious fatalities. Existing fatigue detection works focus on vision and electroencephalography(EEG) based means of detection. However, vision-based approaches suffer from view-blocking or vision distortion problems and EEG-based systems are intrusive and the drivers have to use/wear the devices with inconvenience or additional costs. In this paper, we propose a novel Wi-Fi signals based fatigue detection approach, called WiFind to overcome the drawbacks as associated with the current works. WiFind is simple and (wearable) device-free. It can detect the fatigue symptoms in the vehicle without relying on any visual image or video. By applying peak recognition and SVM-based method, it can recognize driver fatigue according to the body features of drivers both in breath mode and motion mode. We deploy WiFind in commodity Wi-Fi infrastructure and evaluate its performance in real driving environments. The results show WiFind can achieve a recognition accuracy of 82.1\% in a single driver scenario.
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