I-Corps: Translation Potential of Voice Analysis to Pre-screen Airborne Diseases

This I-Corps project focuses on the development of a non-invasive digital health solution that uses voice and biometric signals collected from smart devices to pre-screen for respiratory illnesses such as respiratory syncytial virus (RSV), influenza, and COVID-19. The technology addresses a growing national health concern: the delayed detection and spread of airborne diseases, which strain healthcare systems, reduce workplace productivity, and threaten public health—particularly in crowded or high-risk environments like schools, airports, and hospitals.

The solution aims to empower individuals with early warning tools, allowing them to take preventative action before symptoms worsen or spread to others. By minimizing unnecessary clinic visits, enabling quicker triage, and supporting population-level monitoring, this technology promotes national health and welfare while contributing to more resilient and responsive healthcare infrastructures.

This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of the technology. This solution is based on the development of a voice-enabled biometric monitoring system powered by machine learning algorithms that analyze deviations from a user's baseline in real time.

The system integrates voice modulations, heart rate, and temperature data, and correlates them with clinically observed patterns of respiratory illness. Recent advances in mobile computing, edge artificial intelligence (AI), and signal processing enable the secure and scalable deployment of this solution across smartphones, wearables, and smart speakers.

Unlike traditional diagnostics, this technology offers a passive and continuous approach to health surveillance, benefiting users through earlier detection, reduced costs, and improved public health coordination.

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.