Abstract
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.
Highlights
We will present the results considering both the Skin Potential Response (SPR) and ECG signals logged from the subjects during the course in the two different urban scenarios, comparing the classification performance obtained with the various Machine Learning (ML) algorithms
A first assessment considering only the SPR signal was carried out to evaluate if the physiological responses of the subjects to traffic situations can be analyzed through the scalogram
The SPR and RR signals are sent to the three binary ML classification algorithms presented previously, which are only used for testing
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Research on stress detection techniques has become more and more important in recent years. These techniques aim to automatically evaluate stress arising in individuals in relation to their health and emotional condition. We focus on the problem of automated stress detection in car drivers, since this is of paramount importance to ensure safety and wellness of professionals and people in their everyday life. As a matter of fact, the effects of emotional stress can lead to health problems [1,2] and risky behavior [3,4]
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