# SpiroMask: Measuring Lung Function Using Consumer-Grade Masks

arXiv: https://arxiv.org/abs/2201.09280 · HTML: https://mlsanity.com/paper/2201.09280/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 100 references that are in the map.
- First posted on arXiv 23 Jan 2022.

## Builds on (most cited first, 5 of 5)

- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Sequence-to-point learning with neural networks for nonintrusive load monitoring](https://mlsanity.com/paper/1612.09106/) (2016)
- [Improving sequence-to-sequence speech recognition training with on-the-fly data augmentation](https://mlsanity.com/paper/1910.13296/) (2019)
- [Understanding and Improving Recurrent Networks for Human Activity Recognition by Continuous Attention](https://mlsanity.com/paper/1810.04038/) (2018)
- [Estimating Respiratory Rate From Breath Audio Obtained Through Wearable Microphones](https://mlsanity.com/paper/2107.14028/) (2021)

## Built on by (most cited first, 1 of 1)

- [SpiroActive: Active Learning for Efficient Data Acquisition for Spirometry](https://mlsanity.com/paper/2410.22950/) (2024)

## Abstract (arXiv)

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.