WiFind: Driver Fatigue Detection with Fine-Grained Wi-Fi Signal Features

Driver fatigue is a leading factor in road accidents that can cause severe 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 sy...

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Bibliographic Details
Published in:IEEE transactions on big data 2020-06, Vol.6 (2), p.269-282
Main Authors: Jia, Weijia, Peng, Hongjian, Ruan, Na, Tang, Zhiqing, Zhao, Wei
Format: Article
Language:English
Subjects:
channel state information
Driver fatigue
Driver fatigue detection
Electroencephalography
Fatigue
Feature extraction
Feature recognition
Hilbert transformation
Monitoring
Motion perception
OFDM
Vehicles
Vision
Wireless communication
Wireless fidelity
wireless signal processing
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Summary:Driver fatigue is a leading factor in road accidents that can cause severe 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 our work, 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 self-adaptive method, it can recognize the body features of drivers in multiple modes. It applies Hilbert-Huang transform (HHT) based pattern extract method results in accuracy increase in motion detection mode. WiFind can be easily deployed in a commodity Wi-Fi infrastructure, and we have evaluated its performance in real driving environments. The experimental results have shown that WiFind can achieve the recognition accuracy of 89.6 percent in a single driver scenario.
ISSN:2332-7790
2372-2096
DOI:10.1109/TBDATA.2018.2848969