Estimation of Respiration Rate from Three-Dimensional Acceleration Data Based on Body Sensor Network
Open accessAbstract
Respiratory monitoring is widely used in clinical and healthcare practice to detect abnormal cardiopulmonary function during ordinary and routine activities. There are several approaches to estimate respiratory rate, including accelerometer(s) worn on the torso that are capable of sensing the inclination changes due to breathing. In this article, we present an adaptive band-pass filtering method combined with principal component analysis to derive the respiratory rate from three-dimensional acceleration data, using a body sensor network platform previously developed by us. In situ experiments with 12 subjects indicated that our method was capable of offering dynamic respiration rate estimation during various body activities such as sitting, walking, running, and sleeping. The experimental studies also suggested that our frequency spectrum-based method was more robust, resilient to motion artifact, and therefore outperformed those algorithms primarily based on spatial acceleration information.
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Both PAT and PTT depend on vascular system geometry, elasticity, SBP, and other factors.
“Ultimately, both PAT and PTT depend on vascular system geometry, elasticity, SBP, and other factors30,33,34.”
From Skin-interfaced biosensors for advanced wireless physiological monitoring in neonatal and pediatric intensive-care units (2020) · cites this paper as [34]
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- Authors
- Liu, G.-Z., Guo, Y.-W., Zhu, Q.-S., Huang, B.-Y., & Wang, L.
- Affiliations
- Shenzhen Institutes of Advanced Technology
- Chinese Academy of Sciences (CN)
- Corresponding author
- Wang, Lei
- ORCID
- Wang, Lei
- Venue
- Telemedicine and e-Health
- Publisher
- SAGE Publications
- Published
- 2011-11
- Volume
- 17
- Issue
- 9
- Pages
- 705-711
- ISSN
- 1530-5627 (ISSN-L)
- 1556-3669
- PMC
- PMC3203837
- OpenAlex
- W1995879193
- Cited by
- 107
- References
- 25
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 15 · 2023: 4 · 2022: 15 · 2021: 10 · 2020: 20 · 2019: 5 · 2018: 4 · 2017: 10 · 2016: 5 · 2014: 4 · 2013: 3
- FWCI
- 1.32
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Telemedicine Journal and e-Health (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Keywords
- respiratory rate estimation, wireless body area networks, principal component analysis, motion artifacts, frequency spectrum analysis, wearable sensors, respiratory monitoring
- MeSH terms
- Adult, Algorithms, Equipment Design, Humans, Sleep, Reproducibility of Results, Walking, Monitoring, Ambulatory, Principal Component Analysis, Respiratory Rate
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