Driver fatigue and drunk driving are the leading causes of car accidents. To reduce car accidents, researchers suggest using Internet of Things (IoT) technology to detect in-vehicle air quality and alcohol-impaired effects that lead to driving fatigue and driving drunk. Consequently, in this study, we developed an IoT driver fatigue system based on a ThinkGear ASIC Module (TGAM) for capturing the driver’s electroencephalogram (EEG) signals. Additionally, the developed IoT system deployed SGP30 to detect in-vehicle carbon dioxide $\left(\mathrm{CO}_{2}\right)$ and PMS5003 to measure PM10 concentration. Furthermore, we deployed an MQ3 sensor to monitor driver alcohol consumption. The real-time in-vehicle air quality data and EEG data are displayed on a Node-RED webpage. The proposed system for driver fatigue detection also included a Raspberry Pi model B with 8G of RAM.


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    Titel :

    Driver Fatigue Detection Using TGAM EEG Signal Processing Module


    Beteiligte:
    Lin, Liang-Sian (Autor:in) / Tsai, Hsiang-Yu (Autor:in) / Li, Yi-Jie (Autor:in) / Chen, Hao-Hsuan (Autor:in) / Li, Ling-Ling (Autor:in)


    Erscheinungsdatum :

    2023-10-27


    Format / Umfang :

    985774 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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