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.

项目摘要 在Covid-19大流行期间，医护人员（HCW）感染了11倍以上 风险比普通人群。 HCW之间的Covid-19感染的几个危险因素已经是 确定的，包括缺乏个人保护设备（PPE）和PPE使用不足。其中 因素，PPE的使用不足与感染风险高三分之一有关。给定高 感染的发病率，至关重要的需要解决监测和促进依从性的挑战 在HCW中使用适当的PPE。这项研究的长期目标是减少工作场所的获得性 HCW中的感染是通过改善对具有高传输风险的设置中适当使用的依从性。 该建议的总体目标是设计，实施和测试系统（计算机辅助PPE） （1）使用计算机跟踪团队的PPE依从性的不遵守监控和检测 视觉和（2）突出显示了视频监测系统上潜在的不遵守发作的发作。我们的中心 假设是，多个HCWS对PPE使用的连续监测是一个复杂的，认知要求的， 和容易出错的任务不受当前用于监视PPE依从性的方法。理由 建议是增强对PPE不遵守的认识是减少可传播的要求 HCWS感染。在初步数据的指导下，将通过追求两个特定的特定假设来检验中心假设 目的：（1）设计和实施计算机视觉系统（盖帽），以识别ppe不遵守 动态，基于团队的设置和（2）使用模拟复苏期间的人类绩效 直接观察，基本视频监视和计算机辅助监视（封顶系统）。第一个 AIM，将采用机器学习方法来识别非贴心PPE的类型（头饰， 眼镜，面具，礼服，手套）和不遵守的类别（不存在或不足）。第二 AIM，将设计和评估视觉界面，以监视和聚焦PPE不遵守 人类。拟议的研究具有创新性，因为它解决了 简单地识别多种类型的PPE的不牢固性，由多个个体在动态中使用 环境。这项拟议的研究很重要，因为预计它将减少感染向HCW的传播 通过跟踪并有时提醒他们使用非保护PPE的使用。这项研究的结果有望 通过解决当前方法的局限性，对HCWS的工作场所安全性产生积极影响 监视。