Non-invasive respiration rate monitoring using a single COTS TX-RX pair
Abstract
Respiratory rate is an important vital sign that can indicate progression of illness but to also predict rapid decline in health. For the purpose, non-contact monitoring systems are becoming more popular due to the self-evident increase in patient comfort. As a cost effective solution for non-invasive breathing monitoring, utilizing the received signal strength measurements of inexpensive transceivers has been proposed. However, the applicability of the available solutions is limited since they rely on numerous sensors. In this work, considerable improvement is made, and a respiratory rate monitoring system based on a single commercial off-the-shelf transmitter-receiver pair is presented. Methods that enable estimation and enhance the accuracy are presented and their effects are evaluated. Moreover, it is empirically demonstrated that the performance of the system is comparable to the accuracy of a high-end device for 3–4 orders of magnitude less price; achieving mean absolute error of 0.12 breaths per minute in the most realistic scenario of the experiments.
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High frequency and sub-millimetre wavelength radio technologies have demonstrated the ability to capture physiological signals without body contact.
“In the past decade, high frequency and sub-millimetre wavelength radio technologies have demonstrated the ability to capture physiological signals without body contact.”
From The future of sleep health: a data-driven revolution in sleep science and medicine (2020) · cites this paper as [73]
Through signal processing, it is possible to extract biological information, such as breathing patterns, heart rate and full-body motion, from radio signals.
“Through signal processing, it is possible to extract biological information, such as breathing patterns, heart rate and full-body motion from these findings71,73–75.”
From The future of sleep health: a data-driven revolution in sleep science and medicine (2020) · cites this paper as [73]
Cites
Respiratory rate predicts cardiopulmonary arrest for internal medicine inpatients.
Fieselmann, J. F., Hendryx, M. S., Helms, C. M., & Wakefield, D. S. (1993) · Journal of General Internal Medicine · doi:10.1007/bf02600071 · Cited by 399 · see citing works
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Wireless Sensing for Human Activity: A Survey
Liu, J., Liu, H., Chen, Y., Wang, Y., & Wang, C. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2934489 · Cited by 414 · see citing works
The future of sleep health: a data-driven revolution in sleep science and medicine
Perez-Pozuelo, I., Zhai, B., Palotti, J., Mall, R., Aupetit, M., Garcia-Gomez, J. M., Taheri, S., Guan, Y., & Fernandez-Luque, L. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0244-4 · Cited by 311 · see citing works
- Authors
- Kaltiokallio, O., Yigitler, H., Jantti, R., & Patwari, N.
- Affiliations
- Aalto University (FI)
- University of Utah (US)
- Venue
- IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks
- Publisher
- IEEE
- Published
- 2014-04
- Pages
- 59-69
- OpenAlex
- W4253962816
- Cited by
- 101
- References
- 1
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 12 · 2023: 10 · 2022: 6 · 2021: 9 · 2020: 8 · 2019: 13 · 2018: 15 · 2017: 9 · 2016: 9 · 2015: 5 · 2014: 1
- FWCI
- 15.54
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN) (publishedVersion)
- Funders
- National Science Foundation (1035565, 0855261)
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- respiratory rate estimation, received signal strength, vital signs, low-cost health monitoring
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