MMLung: moving closer to practical lung health estimation using smartphones
MMLung: moving closer to practical lung health estimation using smartphones
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.
2333-2337
International Speech Communication Association
Mosuily, Mohammed
9bd9045b-dff3-4545-b9ab-22960ca5c92f
Welch, Lindsay
2884956f-21b6-47e7-8321-1409f5346cac
Chauhan, Jagmohan
831a12dc-6df9-40ea-8bb3-2c5da8882804
2023
Mosuily, Mohammed
9bd9045b-dff3-4545-b9ab-22960ca5c92f
Welch, Lindsay
2884956f-21b6-47e7-8321-1409f5346cac
Chauhan, Jagmohan
831a12dc-6df9-40ea-8bb3-2c5da8882804
Mosuily, Mohammed, Welch, Lindsay and Chauhan, Jagmohan
(2023)
MMLung: moving closer to practical lung health estimation using smartphones.
In Proc. INTERSPEECH 2023.
International Speech Communication Association.
.
(doi:10.21437/Interspeech.2023-721).
Record type:
Conference or Workshop Item
(Paper)
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.
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mmlung_interspeech_23
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Published date: 2023
Venue - Dates:
Proceedings of the 24th INTERSPEECH Conference, , Dublin, Ireland, 2023-08-20 - 2023-08-24
Identifiers
Local EPrints ID: 491192
URI: http://eprints.soton.ac.uk/id/eprint/491192
PURE UUID: 9c92e671-5a83-43d8-982e-e91f25a2c6a3
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Date deposited: 14 Jun 2024 16:51
Last modified: 15 Jun 2024 02:04
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Contributors
Author:
Mohammed Mosuily
Author:
Jagmohan Chauhan
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