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arXiv · 2410.13623

Neural Correlates of Augmented Reality Safety Warnings: EEG Analysis of Situational Awareness and Cognitive Performance in Roadway Work Zones

Abstract

Despite the research and implementation efforts involving various safety strategies, protocols, and technologies, work zone crashes and fatalities continue to occur at an alarming rate each year. This study investigates the neurophysiological responses to Augmented Reality safety warnings in roadway work zones under varying workload conditions. Using electroencephalogram (EEG) technology, we objectively assessed situational awareness, attention, and cognitive load in simulated low-intensity (LA) and moderate-intensity (MA) work activities. The research analyzed key EEG indicators including beta, gamma, alpha, and theta waves, as well as various combined wave ratios. Results revealed that AR warnings effectively triggered neurological responses associated with increased situational awareness and attention across both workload conditions. However, significant differences were observed in the timing and intensity of these responses. In the LA condition, peak responses occurred earlier (within 125 ms post-warning) and were more pronounced, suggesting a more robust cognitive response when physical demands were lower. Conversely, the MA condition showed delayed peak responses (125-250 ms post-warning) and more gradual changes, indicating a potential impact of increased physical activity on cognitive processing speed. These findings underscore the importance of considering physical workload when designing AR-based safety systems for roadway work zones. The research contributes to the understanding of how AR can enhance worker safety and provides insights for developing more effective, context-aware safety interventions in high-risk work environments.

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BibTeXRIS

Fatemeh Banani Ardecani, Amit Kumar, Sepehr Sabeti, Omidreza Shoghli. 2024-10-17. Neural Correlates of Augmented Reality Safety Warnings: EEG Analysis of Situational Awareness and Cognitive Performance in Roadway Work Zones. https://doi.org/10.1016/j.ssci.2025.106802

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