In this paper, a real-time methodology for the detection of stress events while driving is presented. The detection is based on the use of physiological signals, i.e., electrocardiogram, electrodermal activity, and respiration, as well as past observations of driving behavior. Features are calculated over windows of specific length and are introduced in a Bayesian network to detect driver's stress events. The accuracy of the stress event detection based only on physiological features, evaluated on a data set obtained in real driving conditions, resulted in an accuracy of 82%. Enhancement of the stress event detection model with the incorporation of driving event information has reduced false positives, yielding an increased accuracy of 96%. Furthermore, our methodology demonstrates good adaptability due to the application of online learning of the model parameters.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Real-Time Driver's Stress Event Detection


    Beteiligte:
    Rigas, G. (Autor:in) / Goletsis, Y. (Autor:in) / Fotiadis, D. I. (Autor:in)


    Erscheinungsdatum :

    01.03.2012


    Format / Umfang :

    588941 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Real-Time Driver's Stress Event Detection

    Rigas, G | Online Contents | 2012


    Real-time driver's eye state detection

    Zhichao Tian, / Huabiao Qin, | IEEE | 2005


    Real-Time Based Driver's Drowsiness and Fatigue Detection System

    Srivastava, Ashish / Bansal, Shreya / Sehgal, Satbir S | IEEE | 2022



    Real-time Determination of Driver's Handling Behavior

    Filev, Dimitar / Lu, Jianbo / Hong, Sanghyun | SAE Technical Papers | 2015