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Lung Function Estimation from a Monosyllabic Voice Segment Captured Using Smartphones

Published: 05 October 2020 Publication History

Abstract

Chronic respiratory diseases refer to a group of lung diseases that affect the airways and cause difficulty in breathing. Respiratory diseases are one of the leading causes of death and negatively impact the patients’ quality of life. Early detection and regular monitoring of lung functions might reduce the risk of death; however, lung function assessment requires the active supervision of a medical professional in a clinical setting. To make lung function tests more accessible and ubiquitous, researchers started leveraging mobile devices, which still require active supervision and demand extraneous effort from the user. In this work, we propose a convenient mobile-based approach that uses a monosyllabic voice segment called ‘A-vowel’ sound or ‘Aaaa...’ sound to estimate lung function. We conducted two studies (a lab study and an in-clinic study) with 201 participants to develop a detection model detecting ‘A-vowel’ sound from other acoustic events and a prediction model to estimate the lung function using the detected A-vowel sound. Our study shows that A-vowel sounds can be detected with 93% accuracy, and A-vowel sounds can estimate lung functions with 7.4-11.35% mean absolute error. We also conducted a validation study with 10 participants in a noisy environment and able to detect A-vowel segments with 71% F1-Score. Our results show auspicious directions to expand the horizon of mobile-based lung assessment.

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          cover image ACM Conferences
          MobileHCI '20: 22nd International Conference on Human-Computer Interaction with Mobile Devices and Services
          October 2020
          418 pages
          ISBN:9781450375160
          DOI:10.1145/3379503
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          Publication History

          Published: 05 October 2020

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          1. A-vowel Test
          2. Digital Health
          3. Respiratory Assessment

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          Cited By

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          • (2023)PulmoListenerProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36108897:3(1-24)Online publication date: 27-Sep-2023
          • (2023)FairComp: Workshop on Fairness and Robustness in Machine Learning for Ubiquitous ComputingAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3605107(777-783)Online publication date: 8-Oct-2023
          • (2023)The State of Algorithmic Fairness in Mobile Human-Computer InteractionProceedings of the 25th International Conference on Mobile Human-Computer Interaction10.1145/3565066.3608685(1-7)Online publication date: 26-Sep-2023
          • (2023)A noval pulmonary function evaluation method based on ResNet50 + SVR model and coughScientific Reports10.1038/s41598-023-49334-413:1Online publication date: 12-Dec-2023
          • (2022)A forced cough sound based pulmonary function assessment method by using machine learningFrontiers in Public Health10.3389/fpubh.2022.101587610Online publication date: 25-Oct-2022
          • (2021)BreathTrackProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34781235:3(1-22)Online publication date: 14-Sep-2021

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