SBIR Phase I: Airborne Contagion Mapping through Visual Exhale Monitoring

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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.