RespEar: Earable-Based Robust Respiratory Rate Monitoring
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| Titel: | RespEar: Earable-Based Robust Respiratory Rate Monitoring |
|---|---|
| Autoren: | Liu, Yang, Butkow, Kayla-Jade, Stuchbury-Wass, Jake, Pullin, Adam, Ma, Dong, Mascolo, Cecilia |
| Weitere Verfasser: | Apollo - University of Cambridge Repository |
| Quelle: | 2025 IEEE International Conference on Pervasive Computing and Communications (PerCom). :67-77 |
| Publication Status: | Preprint |
| Verlagsinformationen: | IEEE, 2025. |
| Publikationsjahr: | 2025 |
| Schlagwörter: | FOS: Computer and information sciences, Sound (cs.SD), Artificial Intelligence and Robotics, Audio and Speech Processing (eess.AS), Computer Science - Human-Computer Interaction, FOS: Electrical engineering, electronic engineering, information engineering, Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing, Human-Computer Interaction (cs.HC) |
| Beschreibung: | Continuous respiratory rate (RR) monitoring is essential for understanding physical and mental health, as well as tracking fitness. However, performing reliable and non-obtrusive RR monitoring across diverse daily routines and activities is still an open research problem. In this work, we present RespEar, a pipeline for robust RR monitoring across various sedentary and active scenarios using earphones. RespEar relies solely on in-ear microphones, repurposing them for continuous RR monitoring purposes. Specifically, leveraging the unique properties of in-ear audio, RespEar enables the use of respiratory sinus arrhythmia (RSA) and locomotor respiratory coupling (LRC), physiological couplings between cardiovascular activity, gait and respiration, to determine the RR. This effectively addresses the challenges posed by the almost imperceptible breathing signals encountered during common daily activities. Additionally, RespEar uniquely identifies and addresses three key practical issues for the RSA and LRC-based solutions and introduces a suite of meticulously crafted signal processing techniques to enhance the accuracy of RR measurements. With data collected from 18 subjects over 8 activities, RespEar measures RR with a mean absolute error (MAE) of 1.48 breaths per minute (BPM) and a mean absolute percent error (MAPE) of 9.12% in sedentary conditions, and a MAE of 2.28 BPM and a MAPE of 11.04% in active conditions, respectively. To the best of our knowledge, RespEar is the first earable-based system capable of accurately determining RR in a variety of realisticsettings. |
| Publikationsart: | Article Conference object |
| Dateibeschreibung: | application/pdf |
| DOI: | 10.1109/percom64205.2025.00026 |
| DOI: | 10.17863/cam.115527 |
| DOI: | 10.48550/arxiv.2407.06901 |
| Zugangs-URL: | http://arxiv.org/abs/2407.06901 |
| Rights: | STM Policy #29 CC BY CC BY NC ND |
| Dokumentencode: | edsair.doi.dedup.....ff19c2e2110bb7d26ebb0fd7a7ab494f |
| Datenbank: | OpenAIRE |
Nájsť tento článok vo Web of Science | Abstract: | Continuous respiratory rate (RR) monitoring is essential for understanding physical and mental health, as well as tracking fitness. However, performing reliable and non-obtrusive RR monitoring across diverse daily routines and activities is still an open research problem. In this work, we present RespEar, a pipeline for robust RR monitoring across various sedentary and active scenarios using earphones. RespEar relies solely on in-ear microphones, repurposing them for continuous RR monitoring purposes. Specifically, leveraging the unique properties of in-ear audio, RespEar enables the use of respiratory sinus arrhythmia (RSA) and locomotor respiratory coupling (LRC), physiological couplings between cardiovascular activity, gait and respiration, to determine the RR. This effectively addresses the challenges posed by the almost imperceptible breathing signals encountered during common daily activities. Additionally, RespEar uniquely identifies and addresses three key practical issues for the RSA and LRC-based solutions and introduces a suite of meticulously crafted signal processing techniques to enhance the accuracy of RR measurements. With data collected from 18 subjects over 8 activities, RespEar measures RR with a mean absolute error (MAE) of 1.48 breaths per minute (BPM) and a mean absolute percent error (MAPE) of 9.12% in sedentary conditions, and a MAE of 2.28 BPM and a MAPE of 11.04% in active conditions, respectively. To the best of our knowledge, RespEar is the first earable-based system capable of accurately determining RR in a variety of realisticsettings. |
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| DOI: | 10.1109/percom64205.2025.00026 |