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This study examines stress levels in roadway workers utilizing AR-assistedmulti-sensory warning systems under varying work intensities. A high-fidelityVirtual Reality environment was used to replicate real-world scenarios,allowing safe exploration of high-risk situations while focusing on thephysiological impacts of work conditions. Wearable sensors were used tocontinuously and non-invasively collect physiological data, includingelectrodermal activity to monitor stress responses. Analysis of data from 18participants revealed notable differences in EDR between light- andmedium-intensity activities, reflecting variations in autonomic nervous systemactivity under stress. Also, a feature importance analysis revealed that peakand central tendency metrics of EDR were robust indicators of physiologicalresponses, between light- and medium-intensity activities. The findingsemphasize the relationship between AR-enabled warnings, work intensity, andworker stress, offering an approach to active stress monitoring and improvedsafety practices. By leveraging real-time physiological insights, thismethodology has the potential to support better stress management and thedevelopment of more effective safety warning systems for roadway work zones.This research also provides valuable guidance for designing interventions toenhance 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