In this paper, we propose a relatively noninvasive system that can automatically assess the impact of traffic conditions on drivers. We analyze the physiological signals recorded from a set of individuals while driving in a simulated urban scenario in two different traffic scenarios, i.e., with traffic and without traffic. The experiments were carried out in a laboratory located at the University of Udine, employing a driving simulator equipped with a moving platform. We acquired two Skin Potential Response (SPR) signals from the hands of the drivers, and an electrocardiogram (ECG) signal from their chest. In the proposed scheme, the SPR signals are then processed through a Motion Artifact (MA) removal algorithm such that possible motion artifacts arising during the drive are reduced. An analysis considering the scalogram of the single cleaned SPR signal is presented. This signal, along with the ECG, is then fed to various Machine Learning (ML) algorithms. More specifically, some statistical features are extracted from each signal segment which, after being analyzed through a binary ML model, are labeled as corresponding to a stressful situation or not. Our results confirm the applicability of the proposed approach to identify stress in the two scenarios. This is also in accordance with our findings considering the SPR signal scalograms.


    Access

    Download


    Export, share and cite



    Title :

    Exploring Physiological Signal Responses to Traffic-Related Stress in Simulated Driving †



    Publication date :

    2022-01-01



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



    Exploring Physiological Signal Responses to Traffic-Related Stress in Simulated Driving †

    Zontone P. / Affanni A. / Rinaldo R. et al. | BASE | 2022

    Free access

    Physiological responses of women to simulated weightlessness

    Sandler, H. / Winter, D.L. | TIBKAT | 1978


    Driving Styles and Traffic Density Diagnosis in Simulated Driving Conditions

    Tricot, N. / Sonnerat, D. / Popieul, J. C. et al. | British Library Conference Proceedings | 2003


    Driving styles and traffic density diagnosis in simulated driving conditions

    Tricot, N. / Sonnerat, D. / Popieul, J.C. | IEEE | 2002


    Exploring Driver Physiological Response During Level 3 Conditional Driving Automation

    Gluck, Aaron / Deng, Min / Zhao, Yijin et al. | IEEE | 2022