已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

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

形势意识 情境伦理学 脑电图 意识的神经相关物 认知 工作(物理) 心理学 认知心理学 增强现实 应用心理学 计算机科学 人机交互 社会心理学 工程类 神经科学 机械工程 航空航天工程
作者
Fatemeh Banani Ardecani,Amit Kumar,Sepehr Sabeti,Omidreza Shoghli
出处
期刊:Cornell University - arXiv
标识
DOI:10.48550/arxiv.2410.13623
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.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
蓬荜生辉完成签到,获得积分10
刚刚
大力的灵雁应助西瓜妹采纳,获得20
刚刚
YZ发布了新的文献求助10
1秒前
2秒前
Goin完成签到,获得积分10
2秒前
FJLSDNMV发布了新的文献求助10
2秒前
狐妖完成签到,获得积分10
2秒前
淡定钥匙关注了科研通微信公众号
7秒前
SciGPT应助Tracy采纳,获得10
8秒前
moshang发布了新的文献求助10
9秒前
小二郎应助怡心亭采纳,获得10
10秒前
10秒前
14秒前
16秒前
tiptip应助san采纳,获得10
16秒前
Earr完成签到 ,获得积分10
18秒前
希望天下0贩的0应助tianyue采纳,获得10
18秒前
CodeCraft应助天草诺采纳,获得10
19秒前
爆米花应助贪玩的秋寒采纳,获得10
20秒前
20秒前
徐yy完成签到 ,获得积分10
20秒前
南洋发布了新的文献求助10
21秒前
22秒前
深情安青应助憨憨采纳,获得30
22秒前
908328091完成签到,获得积分20
24秒前
科研通AI6.1应助FJLSDNMV采纳,获得10
24秒前
乔_发布了新的文献求助10
25秒前
怡心亭发布了新的文献求助10
28秒前
29秒前
小灰灰完成签到,获得积分10
29秒前
happy完成签到 ,获得积分10
32秒前
33秒前
晒晒太阳完成签到 ,获得积分10
34秒前
sosososo完成签到 ,获得积分10
35秒前
ahslyycky完成签到,获得积分10
36秒前
邓金涛发布了新的文献求助10
39秒前
FIN发布了新的文献求助100
39秒前
39秒前
40秒前
怡心亭完成签到,获得积分10
41秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
PowerCascade: A Synthetic Dataset for Cascading Failure Analysis in Power Systems 2000
Various Faces of Animal Metaphor in English and Polish 800
Signals, Systems, and Signal Processing 610
Photodetectors: From Ultraviolet to Infrared 500
Diagnostic Performance of Preoperative Imaging-based Radiomics Models for Predicting Liver Metastases in Colorectal Cancer: A Systematic Review and Meta-analysis 500
On the Dragon Seas, a sailor's adventures in the far east 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 物理 内科学 复合材料 催化作用 物理化学 光电子学 电极 细胞生物学 基因 无机化学
热门帖子
关注 科研通微信公众号,转发送积分 6348031
求助须知:如何正确求助?哪些是违规求助? 8162873
关于积分的说明 17172090
捐赠科研通 5404292
什么是DOI,文献DOI怎么找? 2861702
邀请新用户注册赠送积分活动 1839457
关于科研通互助平台的介绍 1688778