DEVELOPMENT OF A VIDEO-BASED PERSONAL PROTECTIVE EQUIPMENT MONITORING SYSTEM

基于视频的个人防护装备监控系统的开发

• 批准号: 10644164
• 负责人: RANDALL S. BURD
• 金额: $ 65.72万
• 依托单位: CHILDREN'S RESEARCH INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10644164
• 关键词: Accident and Emergency department; Address; Adherence; Adopted; Aerosols; Area; COVID-19; COVID-19 pandemic; COVID-19 risk; Categories; Centers for Disease Control and Prevention (U.S.); Cessation of life; Clinical; Cognitive; Complex; Computer Assisted; Computer Vision Systems; Computers; Data; Detection; Engineering; Ensure; Evaluation; Exposure to; Feedback; Foundations; Future; General Population; Goals; Guidelines; Health Personnel; Hospitals; Human; Incidence; Individual; Industrialization; Infection; Intensive Care Units; Machine Learning; Masks; Medical; Methods; Monitor; Operating Rooms; Patients; Patients' Rooms; Performance; Physicians; Procedures; Public Health; Recommendation; Research; Research Personnel; Resources; Resuscitation; Risk; SARS-CoV-2 infection; Safety; System; Testing; Time; Training; Video Recording; Virus Diseases; Visual; Work; Workload; Workplace; base; clinical practice; computer human interaction; deep learning model; design; high risk; human centered design; human-in-the-loop; improved; infection risk; innovation; machine learning method; multidisciplinary; pandemic disease; personal protective equipment; transmission process; viral transmission; ward

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.

项目概要 在 COVID-19 大流行期间，医护人员 (HCW) 的感染率增加了 11 倍多 医护人员感染 COVID-19 的风险高于一般人群。 已发现，其中包括缺乏个人防护装备（PPE）和个人防护装备使用不足。 由于个人防护装备使用不当，感染风险增加三分之一。 感染发生率，迫切需要解决监测和促进依从性的挑战 这项研究的长期目标是减少工作场所获得性感染。 通过提高在传播高风险环境中正确使用个人防护装备的依从性，减少医护人员感染。 该提案的总体目标是设计、实施和测试一个系统（计算机辅助 PPE 不遵守监测和检测 - CAPPED）（1）使用计算机跟踪团队的个人防护装备遵守情况 愿景和 (2) 在我们的中央视频监控系统上突出显示潜在的个人防护装备不遵守情况。 假设是，持续监测多名医护人员的个人防护用品使用情况是一项复杂的、需要认知的、 当前监测个人防护装备遵守情况的方法无法解决容易出错的任务。 提案认为，加强对不遵守个人防护装备的认识是减少传播的必要条件 在初步数据的指导下，中心假设将通过两个具体的研究进行检验。 目标：(1) 设计并实现计算机视觉系统 (CAPPED)，用于识别不遵守 PPE 的情况 动态的、基于团队的设置，以及 (2) 使用以下方法比较模拟复苏过程中的人类表现 直接观察、基本视频监控和计算机辅助监控（CAPPED 系统）。 目标是，机器学习方法将用于识别非粘附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（缺乏或不充分）。 目标是，将设计和评估一个可视化界面，用于监控和突出个人防护装备 (PPE) 不遵守情况 所提出的研究具有创新性，因为它解决了以下挑战： 同时识别动态中多个人使用的几种个人防护装备的不遵守情况 这项拟议的研究意义重大，因为它有望减少医护人员的感染传播。 通过跟踪并最终提醒他们不遵守个人防护装备的使用情况，这项研究的结果预计将 通过解决当前个人防护装备方法的局限性，对医护人员的工作场所安全产生积极影响 监控。