增强现实安全警示的神经关联：道路作业区情境意识与认知绩效的脑电图分析
Neural Correlates of Augmented Reality Safety Warnings: EEG Analysis of Situational Awareness and Cognitive Performance in Roadway Work Zones
AI总结：
本研究利用EEG分析道路作业区不同体力负荷下AR安全警示的神经反应，发现低负荷下认知响应更快更强，高负荷下响应延迟，为设计情境感知的AR安全系统提供了依据。
摘要：
尽管在各类安全策略、规程和技术方面开展了大量研究与实施工作，作业区碰撞事故和死亡事件每年仍以惊人的速度持续发生。本研究考察了在不同工作负荷条件下，道路作业区中增强现实（AR）安全警示引发的神经生理反应。利用脑电图（EEG）技术，我们在模拟的低强度（LA）和中强度（MA）工作活动中客观评估了情境意识、注意力和认知负荷。研究分析了包括β波、γ波、α波和θ波在内的关键EEG指标，以及多种组合波比率。结果表明，AR警示在两种工作负荷条件下均有效触发了与情境意识和注意力提升相关的神经反应。然而，这些反应在时间和强度上存在显著差异。在LA条件下，峰值反应出现得更早（警示后125毫秒内）且更为显著，表明在身体需求较低时认知反应更为稳健。相反，MA条件表现出延迟的峰值反应（警示后125-250毫秒）和更渐进的变化，表明增加的体力活动可能对认知处理速度产生影响。这些发现强调了在为道路作业区设计基于AR的安全系统时考虑身体工作负荷的重要性。本研究有助于理解AR如何提升工人安全，并为在高风险工作环境中开发更有效、具备情境感知能力的安全干预措施提供了见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
