  According to the World Health Organisation (WHO), 235 million people suffer
from respiratory illnesses and four million people die annually due to air
pollution. Regular lung health monitoring can lead to prognoses about
deteriorating lung health conditions. This paper presents our system SpiroMask
that retrofits a microphone in consumer-grade masks (N95 and cloth masks) for
continuous lung health monitoring. We evaluate our approach on 48 participants
(including 14 with lung health issues) and find that we can estimate parameters
such as lung volume and respiration rate within the approved error range by the
American Thoracic Society (ATS). Further, we show that our approach is robust
to sensor placement inside the mask.

Human-Computer Interaction;Machine Learning