MMLung: Moving Closer to Practical Lung Health Estimation using Smartphones.

Abstract

Long-term respiratory illnesses like Chronic Obstructive Pulmonary Disease (COPD) and Asthma are commonly diagnosed with the gold standard spirometry, which is a lung health test that requires specialized equipment and trained healthcare experts, making it expensive and difficult to scale. Moreover, blowing into a spirometer can be quite hard for people suffering from pulmonary illnesses. To solve the aforementioned limitations, we introduce MMLung, an approach that leverages information obtained from multiple audio signals by combining multiple tasks and different modalities performed on the microphone of a smartphone to estimate lung function. Our proposed approach achieves the best mean absolute percentage error (MAPE) of 1.3% on a cohort of 40 participants. Compared to the reported performances (5%-10% MAPE) on lung health estimation using smartphones, MMLung shows that practical lung health estimation is viable by combining multiple tasks utilizing multiple modalities.