Fast and credible identification and estimation of driver's stress-level and stress-type from sensed physiological signals has been one of the critical research areas in the recent past. Several good metrics and mechanisms involving bioelectric signals like the Galvanic Skin Response (GSR), Electrocardiogram (ECG) and the Photoplethysmography (PPG) have been identified by the scholars over the years. This paper discusses the features extracted from physiological data collected in five different scenarios and their usefulness with the help of statistical trend analysis methods. The algorithm developed comprises of a novel shape-based feature weight allocation approach and a technique for credible online realtime stress-trend detection. Such a stress-trend detection by the mesh of embedded sensory elements residing in the e-fabric of a wearable computing system will help in reducing chances of fatal driving errors by the way of in-time activation of alerts and actuation of corresponding safety / recovery procedures.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    An approach for real-time stress-trend detection using physiological signals in wearable computing systems for automotive drivers


    Beteiligte:
    Singh, R. R. (Autor:in) / Conjeti, S. (Autor:in) / Banerjee, R. (Autor:in)


    Erscheinungsdatum :

    01.10.2011


    Format / Umfang :

    352171 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Trend detection in automotive computing

    Boelen, A.J. / Rutten, B.J.C.N. / Meijer, G.R. | Tema Archiv | 2000


    Trend detection in automotive computing

    Boelen,A.J. / Rutten,B.J. / Meijer,G.R. et al. | Kraftfahrwesen | 2000




    Processing of physiological signals in automotive research

    Dambier, Michael / Altmüller, Tobias / Ladstätter, Ulrich | Tema Archiv | 2006