Exploring Physiological Signal Responses to Traffic-Related Stress in Simulated Driving
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 traf...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/3/939 |
| _version_ | 1797484767705300992 |
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| author | Pamela Zontone Antonio Affanni Alessandro Piras Roberto Rinaldo |
| author_facet | Pamela Zontone Antonio Affanni Alessandro Piras Roberto Rinaldo |
| author_sort | Pamela Zontone |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T23:09:11Z |
| format | Article |
| id | doaj.art-ca76cc0c939548f4befadf8ef7e78a2c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T23:09:11Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ca76cc0c939548f4befadf8ef7e78a2c2023-11-23T17:47:53ZengMDPI AGSensors1424-82202022-01-0122393910.3390/s22030939Exploring Physiological Signal Responses to Traffic-Related Stress in Simulated DrivingPamela Zontone0Antonio Affanni1Alessandro Piras2Roberto Rinaldo3Polytechnic Department of Engineering and Architecture, University of Udine, Via delle Scienze 206, 33100 Udine, ItalyPolytechnic Department of Engineering and Architecture, University of Udine, Via delle Scienze 206, 33100 Udine, ItalyPolytechnic Department of Engineering and Architecture, University of Udine, Via delle Scienze 206, 33100 Udine, ItalyPolytechnic Department of Engineering and Architecture, University of Udine, Via delle Scienze 206, 33100 Udine, ItalyIn 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.https://www.mdpi.com/1424-8220/22/3/939stress detection in driverselectrodermal activityelectrocardiogrammotion artifact removalMachine Learning |
| spellingShingle | Pamela Zontone Antonio Affanni Alessandro Piras Roberto Rinaldo Exploring Physiological Signal Responses to Traffic-Related Stress in Simulated Driving Sensors stress detection in drivers electrodermal activity electrocardiogram motion artifact removal Machine Learning |
| title | Exploring Physiological Signal Responses to Traffic-Related Stress in Simulated Driving |
| title_full | Exploring Physiological Signal Responses to Traffic-Related Stress in Simulated Driving |
| title_fullStr | Exploring Physiological Signal Responses to Traffic-Related Stress in Simulated Driving |
| title_full_unstemmed | Exploring Physiological Signal Responses to Traffic-Related Stress in Simulated Driving |
| title_short | Exploring Physiological Signal Responses to Traffic-Related Stress in Simulated Driving |
| title_sort | exploring physiological signal responses to traffic related stress in simulated driving |
| topic | stress detection in drivers electrodermal activity electrocardiogram motion artifact removal Machine Learning |
| url | https://www.mdpi.com/1424-8220/22/3/939 |
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