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

这一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指导系统的商业化，提高动态建筑工地的工人安全和生产率。如果成功，这个项目预计将从根本上改变1.2万亿美元的美国建筑业的工作危险安全管理，在这个行业中，每个工作日大约有三到四名工人死亡。这项拟议的技术有望帮助业主、承包商和施工经理为动态建筑工作做出更好、更安全的决策。此外，本研究还可以提供关于工人安全状况的有价值的数据，这对个人的健康以及安全管理和整个社会都是有益的。收集到的数据将保存公司内部的安全知识，并帮助将知识从经验丰富的专业人员转移到新一代。这项研究计划亦会提供机会，让来自不同社会经济背景的人数偏低的学生，了解建造业的工程问题，并向他们灌输科学知识，以解决这些问题。建议的计划旨在提供一套全面的解决方案，以明智地监察个别工人暴露在危险和不安全状态下的情况。对于一个具有动态和瞬变工作环境特征的系统，要想在施工中可行并在实践中被接受，该系统需要满足许多因素：成本、易用性、可扩展性、实时能力、网络连通性、外形因素、系统部署、准确性、可靠性和私密性。在满足这些因素的同时，建筑信息模型(BIM)驱动物联网(IoT)应用程序将作为拟议的PFI项目中用户界面和传感器跟踪算法的中央平台。具体地，当设置施工工作时间表并将其并入BIM时，提取每日和每周工作活动信息。这些数据可用于根据施工进度提前自动或半自动识别潜在危险。除了基于位置的安全风险外，工人的姿势工作条件还通过使用机器学习算法的动作识别功能进行监控。监测和量化的信息可以提供关于工人安全状况的有价值的数据，使我们能够以定量的方式了解每个工人的健康和安全相关行为，从而为工作场所提供客观、全面的安全强化系统。该奖项反映了NSF的法定使命，并通过使用基金会的智力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.