Collaborative Research: Measuring Attention, Working Memory, and Visual Perception To Reduce Risk of Injuries in the Construction Industry

合作研究：测量注意力、工作记忆和视觉感知以降低建筑行业受伤风险

• 批准号: 1824238
• 负责人: Behzad Esmaeili
• 金额: $ 31.05万
• 依托单位: George Mason University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1824238&HistoricalAwards=false
• 关键词: Collaborative; Research; Measuring; Attention; Working

Human error (e.g., poor decisions or unsafe actions) are a main casual factor in up to 80% of all workplace accidents across a breadth of industries. To the extent our limited capacity for information processing capacity is a major source of such errors, better understanding of cognitive processes will yield more effective methods for predicting and reducing the poor decisions that put workers at risk. Accordingly, this study will complete a series of eye-tracking experiments to build an error-detection framework - the Human-Error Detection Framework - that computes the likelihood of human error in occupational settings to enable proactive countermeasures to keep workers safe. Subsequently, to extend the value of this framework, this project will enrich and expand research-based educational materials, outreach, and engagement activities to spread awareness about this framework to communities and workers. To achieve these goals, this multidisciplinary project blends research linking eye movements and workers' attention with research focused on working-memory load and decision making in order to discover how and why workers in dynamic work environments fail to detect, comprehend, and/or respond to physical risks. Using the dynamic and high-risk environment of construction as a testbed, the proposed framework will connect eye movements and cognitive manipulations in laboratory and field experiments with worker demographics to identify precursors that predict accident-causing human errors in dynamic worksites. In all, this project will demonstrate the value and effectiveness of synthesizing cognitive psychology, engineering, and advanced computation to improve decision making and occupational safety.The Human-Error Detection Framework will harness real-time eye-movement patterns to identify human errors and thereby lay the foundation for synthesizing technology with data analysis to automatically identify and interrupt human decision-making errors before injuries occur. Using the predictive models resulting from this study will not only contribute to significant accident reduction but will also provide a critical validation measure to confirm the effectiveness of training programs in enhancing workers' risk-analysis skills. Furthermore, since this project provides tools and insights for researchers, students, and workers to use to enhance occupational safety and multidisciplinary research, this project will evolve the broader pedagogical landscape of the decision, risk, and management sector. As this innovative research challenges the conventional, reactionary paradigm of safety-risk management by enabling the identification of at-risk workers using a measurable indicator of their cognitive processes, i.e., their eye movements, the proposed proactive approach to occupational safety has the potential for averting occupational accidents across industries and thereby will foreseeably prevent the injuries that undermine the well-being of millions of American workers and their families.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人为错误（例如，错误的决定或不安全的行动）是各行各业所有工作场所事故中高达80%的主要偶然因素。在某种程度上，我们有限的信息处理能力是这种错误的主要来源，更好地理解认知过程将产生更有效的方法来预测和减少使工人处于危险之中的错误决策。因此，本研究将完成一系列眼动追踪实验，以建立一个错误检测框架-人为错误检测框架-计算职业环境中人为错误的可能性，以采取积极的对策来保证工人的安全。随后，为了扩大这一框架的价值，该项目将丰富和扩大以研究为基础的教育材料、外联和参与活动，以向社区和工人传播对这一框架的认识。为了实现这些目标，这个多学科项目将眼动和工人注意力的研究与工作记忆负荷和决策的研究相结合，以发现动态工作环境中的工人如何以及为什么无法检测，理解和/或应对身体风险。使用动态和高风险的施工环境作为试验平台，拟议的框架将连接眼动和认知操作在实验室和现场实验与工人人口统计，以确定预测事故的前兆，在动态工地人为错误。总而言之，该项目将展示综合认知心理学、工程学和高级计算的价值和有效性，以改善决策和职业安全。人为错误检测框架将利用实时眼动模式来识别人为错误，从而为综合技术与数据分析奠定基础，以便在伤害发生之前自动识别和中断人为决策错误。使用本研究的预测模型不仅有助于显著减少事故，而且还将提供一个关键的验证措施，以确认培训计划在提高工人的风险分析技能方面的有效性。此外，由于该项目为研究人员，学生和工人提供了用于加强职业安全和多学科研究的工具和见解，因此该项目将发展决策，风险和管理部门的更广泛的教学前景。由于这项创新的研究挑战了传统的，反动的安全风险管理范式，通过使用他们的认知过程的可测量指标，即，他们的眼球运动，提出的积极主动的方法，以职业安全有可能避免跨行业的职业事故，从而将无可避免地防止伤害，破坏了数以百万计的美国工人及其家庭的福祉。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

项目成果

Working-Memory Load as a Factor Determining the Safety Performance of Construction Workers

• DOI: 10.1061/9780784482872.054
• 发表时间: 2020-11
• 期刊: Biology of Reproduction
• 影响因子: 3.6
• 作者: Gentian Liko;B. Esmaeili;Sogand Hasanzadeh;Michael D. Dodd;Rebecca Brock

Human Performance Best Practices in the Electrical Workplace

电气工作场所人类绩效最佳实践

• DOI: 10.1109/esw41045.2019.9024716
• 发表时间: 2019
• 期刊: 2019 IEEE IAS Electrical Safety Workshop (ESW
• 作者: Aroke, Olugbemi;Doherty, Mike;Esmaeili, Behzad
• 通讯作者: Esmaeili, Behzad

Using Worker Characteristics, Personality, and Attentional Distribution to Predict Hazard Identification Performance: A Moderated Mediation Analysis

• DOI: 10.1061/(asce)co.1943-7862.0002295
• 发表时间: 2022-06
• 期刊: Journal of Construction Engineering and Management
• 影响因子: 5.1
• 作者: Olugbemi Aroke;Sogand Hasanzadeh;B. Esmaeili;Michael D. Dodd;Rebecca Brock

The Role of Work Experience on Hazard Identification: Assessing the Mediating Effect of Inattention under Fall-Hazard Conditions

• DOI: 10.1061/9780784482872.055
• 发表时间: 2020-11
• 作者: Olugbemi Aroke;B. Esmaeili;Sogand Hasanzadeh;Michael D. Dodd;Rebecca Brock

The Association between Risk Perception and the Risk-Taking Behaviors of Construction Workers

建筑工人风险认知与冒险行为的关系

• DOI: 10.1061/9780784481288.042
• 发表时间: 2018
• 期刊: Construction Research Congress 2018
• 作者: Dao, Bac;Hasanzadeh, Sogand;Esmaeili, Behzad
• 通讯作者: Esmaeili, Behzad