SBIR Phase I: Airborne Contagion Mapping through Visual Exhale Monitoring

这项小企业创新研究（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中病毒污染物暴露风险的方法，以减少COVID-19等呼吸系统疾病传播带来的公共卫生风险。该项目的目的是通过呼气可视化提高我们对空气传染如何在密闭室内空间传播的理解，了解传播并可能减少工作空间呼吸系统疾病的传播。通过开发一个匿名的基于视觉的网络，这项工作提供了一种有效的数据驱动方法，用于建模和分析呼吸行为如何在现实世界的工作空间中促进病毒传播。拟议的技术旨在确定可能有效的缓解措施，以降低传染病的成本。由此产生的平台将为教育环境中人口稠密的室内空间的呼气传染风险提供实时分析和人工智能驱动的反馈，并为医疗机构中高危人群的露天呼气行为提供新形式的数据驱动评估。这个小企业创新研究（SBIR）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的病毒污染物相关的传播风险的开放性问题，因为它们在现实世界的工作空间中通过呼吸行为传播。虽然雾化的病毒污染物传播已经得到了很好的研究，但理想化的模型对湍流呼气行为、室内交通模式和环境因素之间复杂的相互作用提供了有限的有效分析，这些因素导致了不稳定的空气污染物传播。该项目将提出一种随机方法，通过对三维重建工作空间内呼气流的基于视觉的分析，对空气中病毒污染物的潜在传播进行建模。通过光谱过滤热成像，我们在3-5um光谱范围内隔离和跟踪呼出的二氧化碳，并在通过网络深度摄像机获得的室内空间三维映射点云模型中模拟呼出行为。我们系统的创新之处在于，将室外呼气流量的测量转化为定量度量，评估每次呼气的流量和体积，并模拟潜在的空气污染传播，为测量和跟踪呼气暴露区域提供定量基础。该项目的预期结果是一个数据驱动的多主体呼吸行为分析建模平台，以减轻潜在的传染暴露。该奖项反映了美国国家科学基金会的法定使命，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。

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.