4.8 Article

Ubi-Fatigue: Toward Ubiquitous Fatigue Detection via Contactless Sensing

Journal

IEEE INTERNET OF THINGS JOURNAL
Volume 9, Issue 15, Pages 14103-14115

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2022.3146942

Keywords

Fatigue; Heart beat; Radar; Wireless communication; Wireless sensor networks; Facial features; Sensors; Fatigue detection; ubiquitous; wireless signal

Funding

  1. RGC [CERG 16204418, 16203719, R8015]
  2. Guangdong Natural Science Foundation [2017A030312008]

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Fatigue is a leading factor for traffic accidents and health problems, but its risks are difficult to quantify due to the lack of efficient and reliable detection methods. The Ubi-Fatigue system presented in this study is a comfortable and contactless fatigue monitor that utilizes wireless signals. By combining vital signs and facial features, the system achieves reliable fatigue detection. Experimental results demonstrate that Ubi-Fatigue outperforms other detection systems in terms of accuracy.
Fatigue is believed to be the leading factor for traffic accidents (e.g., fatigue driving) and health problems (e.g., heart disease and diabetes). However, fatigue-related risks are difficult to quantify because there is no efficient and reliable fatigue detection method comparable to blood alcohol testing for drunk drivers. Conventional fatigue detection methods either require wiring of sensors (e.g., EEG and ECG) that are inconvenient or leverage video camera systems that are lighting sensitive and may leak privacy. We present Ubi-Fatigue, a comfortable and contactless fatigue monitor system using wireless signals. Ubi-Fatigue combines both vital signs and facial features to achieve reliable fatigue detection. A series of novel signal recovery algorithms is developed to extract the heartbeat signal and the eye blink signal from the same raw signal captured by the single-antenna radar. We have implemented a fully functional prototype of Ubi-Fatigue using off-the-shelf radar. Twenty volunteers are involved in extensive experiments for a total duration of 480 h with more than 60 h of collected time-series data. The results demonstrate that Fatigue-Radio can reach a detection accuracy of 81.4%, which is higher than ECG- or visual-based fatigue detection systems, and is approximated to the ECG + visual-based fatigue detection system. Ubi-Fatigue is expected to provide a potential solution for smart home applications in the coming days.

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