RSS-based respiratory rate monitoring using periodic Gaussian processes and Kalman filtering

Roland Hostettler, Ossi Kaltiokallio, Yusein Ali, Simo Särkkä, Riku Jäntti

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

10 Citations (Scopus)
267 Downloads (Pure)


In this paper, we propose a method for respiratory rate estimation based on the received signal strength of narrowband radio frequency transceivers. We employ a state-space formulation of periodic Gaussian processes to model the observed variations in the signal strength. This is then used in a Rao-Blackwellized unscented Kalman filter which exploits the linear substructure of the proposed model and thereby greatly improves computational efficiency. The proposed method is evaluated on measurement data from commercially available off the shelf transceivers. It is found that the proposed method accurately estimates the respiratory rate and provides a systematic way of fusing the measurements of asynchronous frequency channels.
Original languageEnglish
Title of host publication25th European Signal Processing Conference (EUSIPCO)
ISBN (Electronic)978-0-9928626-7-1
ISBN (Print)978-1-5386-0751-0
Publication statusPublished - 2017
MoE publication typeA4 Conference publication
EventEuropean Signal Processing Conference - Kos Island, Greece, Kos, Greece
Duration: 28 Aug 20172 Sept 2017
Conference number: 25

Publication series

NameEuropean Signal Processing Conference
ISSN (Electronic)2076-1465


ConferenceEuropean Signal Processing Conference
Abbreviated titleEUSIPCO
Internet address


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