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

基本信息

  • 批准号:
    10585548
  • 负责人:
  • 金额:
    $ 54.02万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-09-01 至 2026-06-30
  • 项目状态:
    未结题

项目摘要

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 customizable 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)的感染率高出11倍以上 比一般人群的风险更大。HCW中COVID-19感染的几个风险因素已经被 这些问题包括缺乏个人防护设备和个人防护设备使用不当。其中 在这些因素中,个人防护设备的使用不足与三分之一的感染风险有关。考虑到高 感染发生率,迫切需要应对监测和促进依从性的挑战 在医护人员中使用适当的个人防护设备。这项研究的长期目标是减少工作场所获得性 通过提高在高传播风险环境中使用适当个人防护设备的依从性,减少医务人员感染。 本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 不遵守监控和检测-CAPPED),(1)使用计算机跟踪团队的PPE遵守情况 视觉和(2)在视频监控系统上突出显示潜在的PPE不遵守事件。我们的中央 假设连续监测多个HCW使用PPE是一项复杂的、认知要求高的 和当前用于监视PPE遵守的方法未解决的容易出错的任务。这样做的理由 一项建议是,加强对不遵守PPE的认识是减少传染性疾病的一项要求, 感染HCW。在初步数据的指导下,中心假设将通过追求两个具体的 目的:(1)设计和实现一个计算机视觉系统(CAPPED),用于识别PPE不遵守, 动态的,基于团队的设置,和(2)在模拟复苏过程中比较人的表现,使用 直接观察、基本视频监视和计算机辅助监测(CAPPED系统)。第一 目的是,机器学习方法将被应用于识别非粘附PPE的类型(头饰, 护目镜、面罩、手术衣、手套)和不依从类别(不依从或不充分)。根据第二个 目的是,将设计和评估一个可定制的可视化界面,用于监测和突出PPE 不遵守“人在回路”的规定拟议的研究是创新的,因为它解决了 同时识别由多个人使用的几种类型的PPE的不依从性的挑战 in a dynamic动态setting设置.这项拟议中的研究意义重大,因为它有望减少感染 通过跟踪并最终提醒他们未遵守PPE的使用来传播给HCW。的结果 研究预计将通过解决以下问题对HCW的工作场所安全产生积极影响: 目前的PPE监测方法。

项目成果

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RANDALL S. BURD其他文献

RANDALL S. BURD的其他文献

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{{ truncateString('RANDALL S. BURD', 18)}}的其他基金

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
  • 财政年份:
    2022
  • 资助金额:
    $ 54.02万
  • 项目类别:
Automatic Workflow Capture & Analysis for Improving Trauma Resuscitation Outcomes
自动工作流程捕获
  • 批准号:
    8761390
  • 财政年份:
    2014
  • 资助金额:
    $ 54.02万
  • 项目类别:
Intention-aware Recommender System for Improving Trauma Resuscitation Outcomes
用于改善创伤复苏结果的意图感知推荐系统
  • 批准号:
    10629162
  • 财政年份:
    2014
  • 资助金额:
    $ 54.02万
  • 项目类别:
Intention-aware Recommender System for Improving Trauma Resuscitation Outcomes
用于改善创伤复苏结果的意图感知推荐系统
  • 批准号:
    10386911
  • 财政年份:
    2014
  • 资助金额:
    $ 54.02万
  • 项目类别:
Intention-aware Recommender System for Improving Trauma Resuscitation Outcomes
用于改善创伤复苏结果的意图感知推荐系统
  • 批准号:
    10163257
  • 财政年份:
    2014
  • 资助金额:
    $ 54.02万
  • 项目类别:
Automatic Workflow Capture & Analysis for Improving Trauma Resuscitation Outcomes
自动工作流程捕获
  • 批准号:
    8902267
  • 财政年份:
    2014
  • 资助金额:
    $ 54.02万
  • 项目类别:
Automatic Workflow Capture & Analysis for Improving Trauma Resuscitation Outcomes
自动工作流程捕获
  • 批准号:
    9113070
  • 财政年份:
    2014
  • 资助金额:
    $ 54.02万
  • 项目类别:
A Paper-Digital Interface for Time-Critical Information Management
用于时间关键信息管理的纸质数字接口
  • 批准号:
    8386105
  • 财政年份:
    2012
  • 资助金额:
    $ 54.02万
  • 项目类别:
Improving Pediatric Trauma Triage Using High Dimensional Data Analysis
使用高维数据分析改进儿科创伤分诊
  • 批准号:
    8111093
  • 财政年份:
    2010
  • 资助金额:
    $ 54.02万
  • 项目类别:
Improving Pediatric Trauma Triage Using High Dimensional Data Analysis
使用高维数据分析改进儿科创伤分诊
  • 批准号:
    7642839
  • 财政年份:
    2010
  • 资助金额:
    $ 54.02万
  • 项目类别:

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