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

目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/或已知危险的现有知识的限制。因此，许多危险无法识别，造成无法管理的风险。为了提高危险识别能力，本研究的重点是了解和利用人类的身体和行为反应，在他们与物理环境系统的相互作用。众所周知，系统内的潜在危险可能导致行动不稳定，最终导致事故。然而，人类行为的不可预测性，提出了一个关键的挑战，利用这种行动的分析，以确定不稳定的系统条件。在这项研究中开发的方法将使集体模式与人类的反应，以估计整个建筑工地的危险地点的可能性进行评估。因此，从这项研究中获得的知识将提高我们利用事故预防响应信息的能力，从而减少与建筑相关的事故造成的伤害和死亡。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K-12学生和代表性不足的学生群体，特别是妇女和少数民族的外联活动。以及工人的集体身体和行为反应模式在多大程度上识别出未识别的危险，以提高施工环境中的安全性能。这项研究的重点是检测导致坠落事故的危险，这是建筑行业中最危险的伤害事件，使用从可穿戴惯性测量传感器捕获的工人运动传感数据。本研究假设，在一个位置的多个工人的平衡和步态明显的集体异常与在该位置的识别/未识别的跌倒危险的存在（和/或风险）的可能性相关。为了检验这一假设，本项目将：1）确定适当的指标，表征由已识别和未识别的危险引起的工人平衡和步态的扰动; 2）模拟评估工人步态和平衡异常的未遂指数（NMI）; 3）调查集体NMI模式与每个位置危险的存在和风险之间的关系; 4）识别和检查适当的传感器网络平台的方法的可扩展的实施;和5）通过其在建筑工地内的应用程序，验证开发的方法的有效性和实用性。

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.