OptiBreathe: an earable-based PPG system for continuous respiration rate, breathing phase, and tidal volume monitoring
OptiBreathe: an earable-based PPG system for continuous respiration rate, breathing phase, and tidal volume monitoring
In the continuous quest to push the boundaries of mobile healthcare and fitness tracking, monitoring respiratory biomarkers emerges as a pivotal frontier. In this paper, we present OptiBreathe, a lightweight on-device earable system designed to decode the respiratory modulations within photoplethysmography (PPG) signals. OptiBreathe computes three clinical respiratory biomarkers towards enabling continuous respiratory health monitoring with wearable devices. In our effort to bridge respiratory research and earable computing, we collected a first-of-its-kind dataset that empowers researchers to explore in-ear PPG alongside gold-standard spirometry-based ground truth in order to measure respiration rate, breathing phases, and tidal volume. OptiBreathe employs multiple algorithms to measure each respiratory parameter, achieving a best mean absolute error (MAE) of 1.96 breaths per minute on respiratory rate. When estimating breathing phases and tidal volume, OptiBreathe attains an MAE of 0.48 seconds on inspiratory time, 0.14 on inhalation-exhalation ratio (inhalation duration divided by exhalation duration), and a best mean absolute percentage error (MAPE) of 17% on tidal volume (averaged across subjects) . This work shows that the best performing algorithm depends on individuals’ unique physiology, and that future research should investigate the relationship between physiological factors and algorithm performances. As we look forward, we highlight the challenges and nuances in harnessing PPG sensors for respiratory monitoring, inviting researchers to build upon our work.
Breathing Phases, Earables, PPG, Tidal Volume
99-106
Romero, Julia
7edb992b-8585-482f-a7ed-8ba3abb3e04d
Ferlini, Andrea
fb45b18c-a528-41f4-8314-ed02aafd39f1
Spathis, Dimitris
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Dang, Ting
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Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Kawsar, Fahim
86edfb8b-08a4-4038-90ed-1f6569eb66db
Montanari, Alessandro
96e7373c-df1c-4f1b-bdc4-d59f55a507a8
28 February 2024
Romero, Julia
7edb992b-8585-482f-a7ed-8ba3abb3e04d
Ferlini, Andrea
fb45b18c-a528-41f4-8314-ed02aafd39f1
Spathis, Dimitris
0090be07-7d72-4ad9-bac9-f1ceb7f6ed72
Dang, Ting
1ef75748-8531-43ef-9edd-9553d0899940
Farrahi, Katayoun
bc848b9c-fc32-475c-b241-f6ade8babacb
Kawsar, Fahim
86edfb8b-08a4-4038-90ed-1f6569eb66db
Montanari, Alessandro
96e7373c-df1c-4f1b-bdc4-d59f55a507a8
Romero, Julia, Ferlini, Andrea, Spathis, Dimitris, Dang, Ting, Farrahi, Katayoun, Kawsar, Fahim and Montanari, Alessandro
(2024)
OptiBreathe: an earable-based PPG system for continuous respiration rate, breathing phase, and tidal volume monitoring.
In,
HOTMOBILE 2024 - Proceedings of the 2024 25th International Workshop on Mobile Computing Systems and Applications.
(HOTMOBILE 2024 - Proceedings of the 2024 25th International Workshop on Mobile Computing Systems and Applications)
.
(doi:10.1145/3638550.3641136).
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Book Section
Abstract
In the continuous quest to push the boundaries of mobile healthcare and fitness tracking, monitoring respiratory biomarkers emerges as a pivotal frontier. In this paper, we present OptiBreathe, a lightweight on-device earable system designed to decode the respiratory modulations within photoplethysmography (PPG) signals. OptiBreathe computes three clinical respiratory biomarkers towards enabling continuous respiratory health monitoring with wearable devices. In our effort to bridge respiratory research and earable computing, we collected a first-of-its-kind dataset that empowers researchers to explore in-ear PPG alongside gold-standard spirometry-based ground truth in order to measure respiration rate, breathing phases, and tidal volume. OptiBreathe employs multiple algorithms to measure each respiratory parameter, achieving a best mean absolute error (MAE) of 1.96 breaths per minute on respiratory rate. When estimating breathing phases and tidal volume, OptiBreathe attains an MAE of 0.48 seconds on inspiratory time, 0.14 on inhalation-exhalation ratio (inhalation duration divided by exhalation duration), and a best mean absolute percentage error (MAPE) of 17% on tidal volume (averaged across subjects) . This work shows that the best performing algorithm depends on individuals’ unique physiology, and that future research should investigate the relationship between physiological factors and algorithm performances. As we look forward, we highlight the challenges and nuances in harnessing PPG sensors for respiratory monitoring, inviting researchers to build upon our work.
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More information
Published date: 28 February 2024
Keywords:
Breathing Phases, Earables, PPG, Tidal Volume
Identifiers
Local EPrints ID: 501134
URI: http://eprints.soton.ac.uk/id/eprint/501134
PURE UUID: a75a44b6-31c5-4829-8def-182ce9889837
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Date deposited: 27 May 2025 16:40
Last modified: 28 May 2025 01:57
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Contributors
Author:
Julia Romero
Author:
Andrea Ferlini
Author:
Dimitris Spathis
Author:
Ting Dang
Author:
Katayoun Farrahi
Author:
Fahim Kawsar
Author:
Alessandro Montanari
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