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Electrodermal Insights into Stress Dynamics of AR-Assisted Safety Warnings in Virtual Roadway Work Zone Environments

Fatemeh Banani Ardecani, Omidreza Shoghli

TL;DR

This paper addresses safety and stress management in roadway work zones by evaluating how AR-enabled multi-sensory warnings affect workers under different physical demands using a VR work-zone simulation. A non-invasive, real-time stress monitoring approach is built around electrodermal activity (EDA) captured by a wristband, analyzed with cvxEDA to separate tonic and phasic components, and compared across light and moderate activities. Key findings show a significant difference in the electrodermal response (EDR) between light and moderate activities, with $p=0.0019$ for the paired t-test and $p=0.0054$ for the Mann-Whitney U test, and that peak amplitude and central tendency metrics are robust indicators of post-warning stress. These results inform the design of stress-aware AR warning systems to improve safety, productivity, and well-being in high-risk roadway work environments.

Abstract

This study examines stress levels in roadway workers utilizing AR-assisted multi-sensory warning systems under varying work intensities. A high-fidelity Virtual Reality environment was used to replicate real-world scenarios, allowing safe exploration of high-risk situations while focusing on the physiological impacts of work conditions. Wearable sensors were used to continuously and non-invasively collect physiological data, including electrodermal activity to monitor stress responses. Analysis of data from 18 participants revealed notable differences in EDR between light- and medium-intensity activities, reflecting variations in autonomic nervous system activity under stress. Also, a feature importance analysis revealed that peak and central tendency metrics of EDR were robust indicators of physiological responses, between light- and medium-intensity activities. The findings emphasize the relationship between AR-enabled warnings, work intensity, and worker stress, offering an approach to active stress monitoring and improved safety practices. By leveraging real-time physiological insights, this methodology has the potential to support better stress management and the development of more effective safety warning systems for roadway work zones. This research also provides valuable guidance for designing interventions to enhance worker safety, productivity, and well-being in high-risk settings.

Electrodermal Insights into Stress Dynamics of AR-Assisted Safety Warnings in Virtual Roadway Work Zone Environments

TL;DR

This paper addresses safety and stress management in roadway work zones by evaluating how AR-enabled multi-sensory warnings affect workers under different physical demands using a VR work-zone simulation. A non-invasive, real-time stress monitoring approach is built around electrodermal activity (EDA) captured by a wristband, analyzed with cvxEDA to separate tonic and phasic components, and compared across light and moderate activities. Key findings show a significant difference in the electrodermal response (EDR) between light and moderate activities, with for the paired t-test and for the Mann-Whitney U test, and that peak amplitude and central tendency metrics are robust indicators of post-warning stress. These results inform the design of stress-aware AR warning systems to improve safety, productivity, and well-being in high-risk roadway work environments.

Abstract

This study examines stress levels in roadway workers utilizing AR-assisted multi-sensory warning systems under varying work intensities. A high-fidelity Virtual Reality environment was used to replicate real-world scenarios, allowing safe exploration of high-risk situations while focusing on the physiological impacts of work conditions. Wearable sensors were used to continuously and non-invasively collect physiological data, including electrodermal activity to monitor stress responses. Analysis of data from 18 participants revealed notable differences in EDR between light- and medium-intensity activities, reflecting variations in autonomic nervous system activity under stress. Also, a feature importance analysis revealed that peak and central tendency metrics of EDR were robust indicators of physiological responses, between light- and medium-intensity activities. The findings emphasize the relationship between AR-enabled warnings, work intensity, and worker stress, offering an approach to active stress monitoring and improved safety practices. By leveraging real-time physiological insights, this methodology has the potential to support better stress management and the development of more effective safety warning systems for roadway work zones. This research also provides valuable guidance for designing interventions to enhance worker safety, productivity, and well-being in high-risk settings.
Paper Structure (9 sections, 3 figures, 1 table)

This paper contains 9 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Method
  3. Experiment Design and Procedure
  4. Apparatus
  5. Participants
  6. Physiological Data Analysis
  7. Result and Discussion
  8. Conclusion
  9. Acknowledgment

Figures (3)

  • Figure 1: Overview of (a) Near Miss scenario in the simulated work zone layout, (b) components of the multi-modal warning delivery system, and (c) immersive VR environment for light and moderate activity.
  • Figure 2: Hardware and software used for (a) warning delivery and (b) data collection.
  • Figure 3: Feature importance for light and moderate activities