SBIR Phase I: Airborne Contagion Mapping through Visual Exhale Monitoring

SBIR 第一阶段：通过视觉呼气监测绘制空气传播传染病图

• 批准号: 2151374
• 负责人: Shane Transue
• 金额: $ 25.59万
• 依托单位: STRIVISION LLC
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2151374&HistoricalAwards=false
• 关键词: SBIR; Phase; Airborne; Contagion; Mapping

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to create a method for monitoring and evaluating exposure risks from airborne viral contaminants to reduce public health risks from respiratory disease transmission such as COVID-19. The aim of this project is to advance our understanding of how airborne contagions are spread within confined interior spaces through exhaling visualization, to understand transmission and potentially reduce workspace respiratory disease transmission. By developing an anonymized vision-based network, this work provides an effective data-driven method for modeling and analysis of how respiratory behaviors contribute to viral transmission within real-world workspaces. The proposed technology aims to identify potentially effective mitigations to reduce communicable disease costs. The resulting platform will provide real-time analysis and AI-driven feedback for exhaled contagion risks for populated interior spaces in educational settings and new forms of data-driven evaluations of open-air exhale behaviors for high-risk populations in healthcare facilities.This Small Business Innovation Research (SBIR) Phase I project aims to address the open problem of how to effectively and quantitatively evaluate transmission risks associated with airborne viral contaminants as they are spread through respiratory behaviors within real-world workspaces. While aerosolized viral contaminant transmission is well-studied, idealized models provide a limited effective analysis of the complex interplay between turbulent exhale behaviors, indoor traffic patterns, and environmental factors that contribute to erratic airborne contaminant transmissions. This project will present a stochastic method for modeling the potential transmission of airborne viral contaminants through vision-based analysis of expiratory flows within 3D reconstructed workspaces. Through spectral filtered thermal imaging, we isolate and track exhaled CO2 within the 3-5um spectral range to model exhale behaviors within 3D mapped point-cloud models of interior spaces obtained through networked depth cameras. The innovation in our system is the adoption of the measurements of exhaling flow in open air into a quantitative metric that evaluates flow and volume per exhale and models potential airborne contamination spread, providing a quantitative foundation for measuring and tracking exhale exposure regions. The expected outcome of this project is a platform for data-driven modeling of multi-subject respiratory behavioral analysis for potential contagion exposure mitigation.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.

该小型企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力是创建一种监测和评估空气传播病毒污染物暴露风险的方法，以减少COVID-19等呼吸道疾病传播的公共卫生风险。该项目的目的是促进我们对空气传播的传染病如何通过呼气可视化在有限的内部空间内传播的理解，以了解传播并可能减少工作场所呼吸道疾病的传播。通过开发一个匿名的基于视觉的网络，这项工作提供了一个有效的数据驱动的方法，用于建模和分析呼吸行为如何有助于在现实世界中的病毒传播。该技术旨在确定潜在的有效缓解措施，以降低传染病成本。由此产生的平台将为教育环境中人口密集的内部空间的呼出传染风险提供实时分析和人工智能驱动的反馈，并为医疗机构中的高风险人群提供新形式的露天呼气行为数据驱动评估。第一阶段项目旨在解决如何有效和定量地评估与空气传播病毒污染物相关的传播风险这一悬而未决的问题，它们通过现实世界工作空间内的呼吸行为传播。虽然气溶胶化病毒污染物的传播是很好的研究，理想化的模型提供了一个有限的有效的分析之间的复杂的相互作用，湍流呼气行为，室内交通模式和环境因素，有助于不稳定的空气污染物的传输。该项目将提出一种随机方法，通过基于视觉的分析三维重建呼吸道内的呼气流量来模拟空气中病毒污染物的潜在传播。通过光谱过滤热成像，我们在3- 5 um光谱范围内隔离和跟踪呼出的CO2，以在通过联网深度相机获得的内部空间的3D映射点云模型内对呼气行为进行建模。我们系统的创新之处在于将露天呼气流量的测量结果转化为定量指标，该指标可评估每次呼气的流量和体积，并对潜在的空气污染扩散进行建模，为测量和跟踪呼气暴露区域提供定量基础。该项目的预期成果是建立一个平台，用于对多受试者呼吸行为分析进行数据驱动建模，以减轻潜在的传染病暴露。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。