Revealing Hidden Safety Hazards Using Workers' Collective Bodily and Behavioral Response Patterns

利用工人的集体身体和行为反应模式揭示隐藏的安全隐患

• 批准号: 1800310
• 负责人: Changbum Ahn
• 金额: $ 16.27万
• 依托单位: Texas A&M University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-16 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1800310&HistoricalAwards=false
• 关键词: Revealing; Hidden; Safety; Hazards; Using

Current hazard-identification efforts in safety management are mostly limited by humans' abilities to recognize hazards and/or by their existing knowledge of known hazards. Consequently, numerous hazards go unidentified, creating unmanageable risks. To enhance hazard recognition capabilities, this research focuses on understanding and exploiting humans' bodily and behavioral responses in their interaction with the physical environmental system. It is well recognized that a potential hazard within the system may cause instability in actions, and ultimately accidents. However, the unpredictable nature of human behavior poses a critical challenge in utilizing the analysis of such actions for the identification of unstable system conditions. Methodologies developed in this research will enable the evaluation of collective patterns associated with human responses to estimate the likelihood of hazard locations across a construction site. Thus, knowledge gained from this research will advance our ability to utilize response information for accident prevention, leading to reduced injuries and fatalities from construction-related accidents. Research outcomes will be integrated into engineering curriculum development, undergraduate research activities, industry workshops, and outreach activities for K-12 students and underrepresented student groups, especially women and minorities.The objective of this research is to examine whether, how, and to what extent workers' collective bodily and behavioral response patterns identify recognized/unrecognized hazards for the purpose of enhancing safety performance in construction environments. This research focuses on detecting hazards that causes fall accidents, a single most dangerous injury event within the construction industry, using workers - kinematic sensing data captured from wearable inertial measurement sensors. This research hypothesizes that the collective abnormalities apparent in multiple workers' balance and gait in one location is correlated with the likelihood of the presence (and/or the risk) of a recognized/unrecognized fall hazard in that location. To test this hypothesis, this project will: 1) identify appropriate metrics that characterize the perturbation to workers' balance and gait caused by recognized and unrecognized hazards; 2) model a near-miss index (NMI) that evaluates the abnormalities of workers' gait and balance; 3) investigate the relationship between the collective NMI patterns and the presence and risk of a hazard in each location; 4) identify and examine appropriate sensor network platforms for the scalable implementation of the approach; and 5) validate the efficacy and usefulness of the developed approach through its application within construction sites.

当前安全管理中的危险识别工作主要受到人类识别危险的能力和/或他们对已知危险的现有知识的限制。因此，许多危险未被识别，造成难以控制的风险。为了提高危险识别能力，本研究的重点是理解和利用人类在与物理环境系统相互作用时的身体和行为反应。众所周知，系统内的潜在危险可能会导致行动不稳定，并最终导致事故。然而，人类行为的不可预测性对利用此类行为的分析来识别不稳定的系统条件提出了严峻的挑战。这项研究开发的方法将能够评估与人类反应相关的集体模式，以估计整个建筑工地发生危险位置的可能性。因此，从这项研究中获得的知识将提高我们利用响应信息预防事故的能力，从而减少建筑相关事故造成的伤亡。研究成果将被纳入工程课程开发、本科生研究活动、行业研讨会以及针对 K-12 学生和代表性不足的学生群体（尤其是女性和少数族裔）的外展活动。这项研究的目的是检查工人的集体身体和行为反应模式是否、如何以及在多大程度上识别已识别/未识别的危险，以提高建筑环境中的安全绩效。这项研究的重点是利用工人从可穿戴惯性测量传感器捕获的运动传感数据来检测导致坠落事故的危险，坠落事故是建筑行业中最危险的伤害事件。这项研究假设，一个地点多名工人的平衡和步态中明显的集体异常与该地点存在已识别/未识别跌倒危险的可能性（和/或风险）相关。为了检验这一假设，该项目将： 1) 确定适当的指标来描述已识别和未识别的危险对工人平衡和步态造成的干扰； 2）建立评估工人步态和平衡异常的未遂指数（NMI）模型； 3) 调查集体 NMI 模式与每个地点危险的存在和风险之间的关系； 4）确定并检查适当的传感器网络平台，以实现该方法的可扩展实施； 5) 通过在建筑工地的应用来验证所开发方法的有效性和实用性。