Collaborative Research: Worksite-specific Safety Training Environments with Augmented Reality

在高风险工作场所，如建筑工地，不注意工作场所的危险是造成严重伤害和死亡的常见因素。证据有力地表明，传统的，以讲座为基础的安全培训方法，在课堂上设置很少捕捉工人的兴趣，并没有减少事故。此外，成年人往往在自己的工作环境和现实生活中学习得最好。这表明，使安全培训与受训者的工作环境更密切地联系起来，可能比目前的方法更有效。为此，该项目将构建一个个性化的增强现实（AR）培训系统，该系统使用受训者在自己的工作场所拍摄的照片来识别现场特定的危险，并创建其工作场所的复制品，显示由这些危险引起的模拟事故。这项工作需要在AR环境设计、计算机视觉算法和工作场所培训教学法方面取得进展，并将导致在高风险行业中更好的职业安全培训实践，以及为高中、大学和行业提供有关职业安全的新教材。该项目将分两个阶段组织。首先，该项目将创建以学习者为中心的、以人工智能为灵感的AR安全培训环境，学员可以自己组织安全培训内容，并在AR环境中体验潜在的事故。第一阶段将包括（1）开发人工智能算法，从受训者的工作场所检测工作场所危险;（2）创建事故场景池，可以适应各种类型，形状和规模的现实世界危险;（3）通过准确和无缝地将AR化身叠加在检测到的现实世界危险上来模拟潜在事故。在第二阶段，该团队将与行业合作伙伴合作，通过（1）评估个别受训者的危险识别能力，（2）监测工人在工作场所危险附近的身体行为，以及（3）将使用所提出的方法训练的个体的表现与使用常规方法以及非常规方法训练的个体的表现进行比较。该奖项反映了NSF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。

In high-risk workplaces, such as construction sites, inattention to workplace hazards a common factor in serious injuries and fatalities. Evidence strongly suggests that conventional, lecture-based safety training methods in classroom settings rarely capture workers’ interest and do not decrease accidents. Further, adults often learn best in the context of their own work environments and real-life situations. This suggests that making safety training more closely associated with trainees’ work environments may be more effective than current methods. To this end, this project will construct a personalized augmented reality (AR) training system that uses pictures taken by trainees on their own worksites to recognize site-specific hazards and create replicas of their worksites that show simulated accidents caused by those hazards. This work will require advances in AR environment design, computer vision algorithms, and workplace training pedagogy, and will lead to better occupational safety training practices in high-risk industries and new educational materials for high schools, universities, and industry around occupational safety.The project will be organized in two phases. First, the project will create learner-centered, andragogy-inspired AR safety training environments wherein trainees organize safety training content by themselves and experience potential accidents in the AR environment. This first phase will include (1) developing AI algorithms that detect workplace hazards from the trainees’ worksites; (2) creating an accident scenario pool, which can be adapted to various types, shapes, and scales of real-world hazards; and (3) simulating potential accidents by accurately and seamlessly overlaying AR avatars on detected real-world hazards. In collaboration with industry partners, during the second phase the team will examine the effect of the developed training on workers’ attention to real-world hazards by (1) assessing individual trainees’ hazard recognition abilities, (2) monitoring workers’ physical behaviors near workplace hazards, and (3) comparing the performance of individuals trained using the proposed methods to those trained using conventional methods as well as non-personalized and virtual reality environments.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.