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，决策或不安全的行动）是多达80％的行业工作场所事故的主要因素。在我们的信息处理能力有限的范围内是此类错误的主要来源，对认知过程的更好理解将产生更有效的方法来预测和减少使工人处于危险中的不良决策。因此，这项研究将完成一系列引人注目的实验，以建立一个错误检测框架 - 人类纠正框架 - 计算职业环境中人为错误的可能性，以实现主动的对策以确保工人的安全。随后，为了扩展该框架的价值，该项目将丰富和扩展基于研究的教育材料，外展和参与活动，以将对这一框架的认识传播给社区和工人。为了实现这些目标，这个多学科的项目将研究与工作记忆负载和决策的研究联系在一起，将研究联系起来，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。将拟议的动态和高风险的构造环境作为测试台，该框架将在实验室和现场实验中使用工人人口统计数据中的眼球运动和认知操作，以识别预测动态工作人员中引起人类错误的人类错误的前体。总共，该项目将证明合成认知心理学，工程和高级计算以改善决策和职业安全的价值和有效性。人类纠正框架将利用实时眼睛移动模式来识别人类错误，从而确定人类错误，从而为技术分析构成数据分析的基础，从而自动识别和中断人类的决策错误。使用本研究产生的预测模型不仅会大大减少事故，而且还将提供一项关键的验证措施，以确认培训计划在增强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增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景观。由于这项创新的研究挑战了安全风险管理的常规，反动范式，通过使用可衡量的认知过程的指标来识别处于危险的工人，即他们的眼睛运动，提议的职业安全方法具有主动行动的职业安全性，可以使他们能够避免跨越工业的事故，从而避免跨越任何工业的事故，使他们无法实现这种界限，而这些事业的范围是不足以使您的受害者及其范围内的企业及其界限家庭。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛影响的评论标准来评估的。

项目成果

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

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

Examining the Relationship between Personality Characteristics and Worker’s Attention under Fall and Tripping Hazard Conditions

检查跌倒和绊倒危险条件下人格特征与工人注意力之间的关系

• DOI: 10.1061/9780784481288.040
• 发表时间: 2018
• 期刊: Louisiana
• 作者: Hasanzadeh, Sogand;Esmaeili, Behzad;Dodd, Michael D.
• 通讯作者: Dodd, Michael D.

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