DEVELOPMENT OF A VIDEO-BASED PERSONAL PROTECTIVE EQUIPMENT MONITORING SYSTEM

项目总结 在新冠肺炎大流行期间，医护人员的感染率增加了11倍以上 风险高于一般人群。医务人员感染新冠肺炎的几个危险因素包括 查明的问题包括缺乏个人防护装备(PPE)和个人防护装备使用不足。其中包括 在这些因素中，个人防护用品的使用不足与感染风险增加三分之一有关。考虑到高价 鉴于感染的发生率，迫切需要解决监测和促进遵守方面的挑战 在卫生工作者中适当使用个人防护用品。这项研究的长期目标是减少工作场所获得性 在传播风险较高的环境中，通过提高对适当个人防护用品使用的遵守程度，改善卫生工作者的感染情况。 本提案的总体目标是设计、实施和测试系统(计算机辅助个人防护设备 不遵守监控和检测(1)使用计算机跟踪团队的PPE遵守情况 VISION和(2)突出了视频监控系统上可能出现的PPE不遵守事件。我们的中央 假设对多个医务工作者使用个人防护用品的持续监测是一项复杂的、对认知要求很高的工作， 以及当前用于监测PPE遵守情况的方法未解决的容易出错的任务。这样做的理由是 建议加强对个人防护装备不粘连的认识，这是减少传染性的一个要求 在卫生工作者中的感染。在初步数据的指导下，中心假设将通过追求两个具体的 目标：(1)设计并实现了一个用于识别PPE未粘连的计算机视觉系统(CAPPED) 动态、基于团队的设置，以及(2)比较人类在模拟复苏过程中的表现 直接观察、基本视频监控和计算机辅助监控(带帽系统)。对于第一次 目的，将应用机器学习方法来识别未粘连的个人防护装备的类型(头饰， 眼镜、口罩、长袍、手套)和不遵守类别(缺席或不适当)。在第二个下面 AIM，将设计和评估用于监测和突出PPE不遵守的可视界面 一个人在圈子里。这项拟议的研究具有创新性，因为它解决了 同时识别动态中多个个人使用的几种PPE类型的不依从性 布景。这项拟议的研究具有重要意义，因为它有望减少对卫生工作者的感染传播 通过跟踪并最终提醒他们不遵守个人防护装备的使用。这项研究的结果预计将 通过解决当前个人防护措施的局限性，积极影响卫生工作者的工作场所安全 监控。

PROJECT SUMMARY During the COVID-19 pandemic, healthcare workers (HCWs) have had a more than 11-fold higher infection risk than the general population. Several risk factors for COVID-19 infection among HCWs have been identified, including the lack of personal protective equipment (PPE) and inadequate PPE use. Among these factors, the inadequate use of PPE has been associated with a one-third higher risk of infection. Given the high incidence of infection, there is a critical need to address the challenges of monitoring and promoting adherence with appropriate PPE use among HCWs. The long-term goal of this research is to reduce workplace-acquired infections in HCWs by improving adherence to appropriate PPE use in settings at high risk of transmission. The overall objectives of this proposal are to design, implement, and test a system (Computer-Aided PPE Nonadherence Monitoring and Detection—CAPPED) that (1) tracks the team’s PPE adherence using computer vision and (2) highlights episodes of potential PPE nonadherence on a video-monitoring system. Our central hypothesis is that continuous monitoring of PPE use by multiple HCWs is a complex, cognitively demanding, and error-prone task unaddressed by current methods for monitoring PPE adherence. The rationale for this proposal is that enhanced recognition of PPE nonadherence is a requirement for reducing transmissible infections in HCWs. Guided by preliminary data, the central hypothesis will be tested by pursuing two specific aims: (1) design and implement a computer vision system (CAPPED) for recognizing PPE nonadherence in a dynamic, team-based setting, and (2) compare human performance during simulated resuscitations using direct observation, basic video surveillance, and computer-aided monitoring (CAPPED system). For the first Aim, machine learning approaches will be applied to recognize the type of nonadherent PPE (headwear, eyewear, mask, gown, gloves) and the category of nonadherence (absent or inadequate). Under the second Aim, a visual interface will be designed and evaluated for monitoring and spotlighting PPE nonadherence with a human-in-the-loop. The proposed research is innovative because it addresses the challenges of simultaneously identifying nonadherence with several types of PPE used by multiple individuals in a dynamic setting. This proposed research is significant because it is expected to reduce infection transmission to HCWs by tracking and eventually alerting them to nonadherent PPE use. The results of this research are expected to positively impact the workplace safety of HCWs by addressing the limitations of current approaches to PPE monitoring.