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Towards Respiration Rate Monitoring Using an In-Ear Headphone Inertial Measurement Unit

Röddiger, Tobias ORCID iD icon 1; Wolffram, Daniel 1; Laubenstein, David 1; Budde, Matthias 1; Beigl, Michael 1
1 Karlsruher Institut für Technologie (KIT)


State-of-the-art respiration tracking devices require specialized equipment, making them impractical for every day at-home respiration sensing. In this paper, we present the first system for sensing respiratory rates using in-ear headphone inertial measurement units (IMU). The approach is based on technology already available in commodity devices: the eSense headphones. Our processing pipeline combines several existing approaches to clean noisy data and calculate respiratory rates on 20-second windows. In a study with twelve participants, we compare accelerometer and gyroscope based sensing and employ pressure-based measurement with nasal cannulas as ground truth. Our results indicate a mean absolute error of 2.62 CPM (acc) and 2.55 CPM (gyro). This overall accuracy is comparable to previous approaches using accelerometer-based sensing, but we observe a higher relative error for the gyroscope. In contrast to related work using other sensor positions, we can not report significant differences between the two modalities or the three postures standing, sitting, and lying on the back (supine). However, in general, performance varies drastically between participants.

Postprint §
DOI: 10.5445/IR/1000118558
Veröffentlicht am 24.09.2020
DOI: 10.1145/3345615.3361130
Zitationen: 31
Zitationen: 30
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Telematik (TM)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 09.2019
Sprache Englisch
Identifikator ISBN: 978-1-4503-6902-2
KITopen-ID: 1000118558
Erschienen in UbiComp '19: The 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing - EarComp'19: Proceedings of the 1st International Workshop on Earable Computing - London, United Kingdom, 9th-13th September 2019
Verlag Association for Computing Machinery (ACM)
Seiten 48–53
Vorab online veröffentlicht am 09.09.2019
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