PFI-TT: Smart pose and position guidance system for construction workers' safety and productivity

该创新技术转化伙伴关系（PFI-TT）项目的更广泛影响/商业潜力是通过实现无线姿势和位置引导系统的商业化，提高动态建筑工地工人的安全和生产力。如果成功，该项目预计将从根本上改变价值1.2万亿美元的美国建筑行业的工作危险安全管理，该行业每天约有三到四名工人死亡。拟议中的技术有望帮助业主、承包商和施工经理为动态施工工作做出更好、更安全的决策。此外，本研究可以提供有关工人安全状况的宝贵数据，这有利于个人健康，安全管理和整个社会。收集的数据将保存公司内部的安全知识，并帮助经验丰富的专业人员将知识传递给新一代。该研究项目还将为来自不同社会经济背景的代表性不足的学生提供一个机会，让他们了解建筑行业中具有挑战性的工程问题，并为他们提供解决这些问题的科学知识。拟议的项目旨在为智能监控个体工人暴露于危险和不安全姿势提供整体解决方案。对于具有动态和瞬态工作环境特征的系统，要使其具有可行性和实际可接受性，系统需要满足许多因素：成本，易用性，可扩展性，实时性，网络连接性，外形因素，系统部署，准确性，可靠性和隐私性。在满足这些因素的同时，在拟议的PFI项目中，建筑信息模型（BIM）驱动的物联网（IoT）应用程序将用作用户界面和传感器跟踪算法的中心平台。特别是在制定施工进度并将其纳入BIM时，提取每日和每周的工作活动信息。这些数据可以根据施工进度自动或半自动地提前识别潜在的危险。除了基于位置的安全风险外，还通过使用机器学习算法的动作识别功能监测工人的姿势工作条件。监测和量化的信息可以提供有关工人安全状况的有价值的数据，使我们能够定量地了解每个工人的健康和安全相关行为，从而为工作现场提供客观，全面的安全强化系统。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Partnerships for Innovation - Technology Translation (PFI-TT) project is to improve worker safety and productivity in dynamic construction job sites by enabling commercialization of a wireless pose and position guidance system. If successful, this project is expected to radically change job hazard safety management for the $1.2 trillion U.S. construction industry in which approximately three to four workers die every working day. The proposed technology is expected to help owners, contractors, and construction managers to make better and safer decisions for dynamic construction work. In addition, this study can provide valuable data regarding the safety condition of workers, which benefits the individuals' health as well as safety management and society in general. The collected data will preserve safety knowledge within the company and help knowledge transfer from experienced professionals to new generations. The research project will also provide an opportunity to engage underrepresented students from diverse socio-economic backgrounds to learn about the challenging engineering problems in the construction industry and equip them with scientific knowledge to address those problems.The proposed project aims to provide a holistic solution for intelligently monitoring individual workers' exposure to hazards and unsafe postures. For a system to be feasible as well as practically acceptable in construction which has dynamic and transient work environmental characteristics, the system needs to satisfy many factors: cost, ease of use, scalability, real-time ability, network connectivity, form factor, system deployment, accuracy, reliability, and privacy. While satisfying the factors, a Building Information Model (BIM)-driven Internet of Things (IoT) application will be used as a central platform for user interface and sensor tracking algorithms in the proposed PFI project. In particular, when the construction work schedules are set and incorporated into BIM, daily and weekly work activity information is extracted. These data can serve to automatically or semi-automatically identify potential hazards in advance based on construction progress. In addition to the location-based safety risks, postural working conditions of the workers are monitored through a motion recognition function using machine learning algorithms. The monitored and quantified information can provide valuable data regarding the safety condition of workers, empower our knowledge about each worker's health and safety-related behavior in a quantitative manner, and thus deliver an objective, comprehensive safety reinforcing system to a job site.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.