Assessing Workers Neuro-physiological Stress Responses to Augmented Reality Safety Warnings in Immersive Virtual Roadway Work Zones

2025-06-03

AI Summary

This study investigates the neurophysiological stress responses of construction workers to augmented reality (AR) safety warnings under varying task loads in an immersive virtual reality (VR) roadwork environment. Method: A multi-stage VR experimental paradigm was developed, concurrently acquiring electrodermal activity (EDA), heart rate variability (HRV), and electroencephalography (EEG). Time-frequency analysis, feature importance ranking, and multimodal correlation modeling were applied to characterize dynamic stress responses. Contribution/Results: The study首次 reveals a task-load–dependent dissociation in autonomic and central nervous system responses to AR alerts: neural markers (α-suppression/β-enhancement) lag significantly behind physiological indicators (EDA elevation, tachycardia), establishing a “physiology-first, cognition-later” temporal coupling mechanism. Mean EDA and short-term heart rate emerged as key biomarkers for task-load classification; moderate workload elicited the most pronounced stress response—highlighting critical thresholds for AR warning design and occupational safety interventions.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Brain-Sensing and Analysis
• Cognitive Modeling & Cognitive Systems: Affective Computing

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

This paper presents a multi-stage experimental framework that integrates immersive Virtual Reality (VR) simulations, wearable sensors, and advanced signal processing to investigate construction workers neuro-physiological stress responses to multi-sensory AR-enabled warnings. Participants performed light- and moderate-intensity roadway maintenance tasks within a high-fidelity VR roadway work zone, while key stress markers of electrodermal activity (EDA), heart rate variability (HRV), and electroencephalography (EEG) were continuously measured. Statistical analyses revealed that task intensity significantly influenced physiological and neurological stress indicators. Moderate-intensity tasks elicited greater autonomic arousal, evidenced by elevated heart rate measures (mean-HR, std-HR, max-HR) and stronger electrodermal responses, while EEG data indicated distinct stress-related alpha suppression and beta enhancement. Feature-importance analysis further identified mean EDR and short-term HR metrics as discriminative for classifying task intensity. Correlation results highlighted a temporal lag between immediate neural changes and subsequent physiological stress reactions, emphasizing the interplay between cognition and autonomic regulation during hazardous tasks.

Problem

• Assessing workers' stress responses to AR safety warnings in VR work zones
• Investigating neuro-physiological markers (EDA, HRV, EEG) during roadway tasks
• Analyzing how task intensity affects stress indicators and autonomic arousal

Innovation

• Immersive VR simulations for stress assessment
• Wearable sensors track neuro-physiological responses
• Advanced signal processing analyzes stress markers