# Advancing telemedicine in pulmonology: acoustic-waveform respiratory evaluation (AWARE) via sensing and machine learning on smartphones

> **NIH NIH R01** · INDIANA UNIVERSITY INDIANAPOLIS · 2024 · $676,586

## Abstract

ABSTRACT
Asthma and COPD respectively affect 25 million and 16 million people in the US. Chronic lower respiratory
diseases represented the fourth leading cause of death in the country before the pandemic. For these and
other pulmonary diseases like cystic fibrosis (CF), monitoring disease remotely but objectively via telemedicine
could lead to marked improvements in disease control, quality of life, and overall prognosis. However, current
disease monitoring and management often rely on subjective symptom report, and objective pulmonary
function tests (PFTs) are often only done once or twice a year at subspecialty referral centers. There are very
few solutions to objectively evaluate lung health out of clinic, and existing portable PFTs have several key
limitations: they require additional equipment that is expensive, cumbersome, or needs frequent calibration;
and/or they rely on forced maximal respiratory maneuvers, which require professional coaching and are
particularly difficult for younger children, older adults, and those with advanced disease. Our highly innovative
solution, AWARE (Acoustic WAveform Respiratory Evaluation), aims to fundamentally advance pulmonary
telemedicine by inventing new acoustic sensing and machine learning techniques that transform everyday
smartphones from a video chat client to a fully functional pulmonary telemedicine examination device. We
hypothesize that AWARE can accurately identify specific airway diseases, closely estimate lung function, and
adaptively detect deviations from normal that are associated with disease exacerbations. We will test these
hypotheses by recruiting a cohort of 750 subjects and completing three specific aims. In Aim 1, we will improve
AWARE’s sensing approach to accurately differentiate participants without chronic airway disease, patients
with asthma, COPD, CF, and other airway diseases. In Aim 2, we will improve AWARE’s accuracy in
estimating lung function indices from spirometry and airway oscillometry, both in healthy participants and in
those with respiratory diseases. In Aim 3, we will develop new techniques to evaluate within-patient changes in
AWARE between stable vs acute exacerbations and provide explainability to the correlation between these
cases by leveraging explainable AI techniques. AWARE will provide an innovative, portable, low-cost, non-
invasive, accurate, reliable, and easy-to-use approach to aid in pulmonary disease diagnosis, lung function
monitoring, and detection of disease exacerbations, providing personalized reports for clinician’s review and
interpretation. Of direct relevance to crises like COVID19, tele-health monitoring with AWARE would reduce
the risk for vulnerable populations by avoiding in-person clinic visits and use of shared PFT equipment.

## Key facts

- **NIH application ID:** 10904494
- **Project number:** 1R01HL170368-01A1
- **Recipient organization:** INDIANA UNIVERSITY INDIANAPOLIS
- **Principal Investigator:** Erick Forno
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $676,586
- **Award type:** 1
- **Project period:** 2024-05-20 → 2029-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10904494

## Citation

> US National Institutes of Health, RePORTER application 10904494, Advancing telemedicine in pulmonology: acoustic-waveform respiratory evaluation (AWARE) via sensing and machine learning on smartphones (1R01HL170368-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10904494. Licensed CC0.

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