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

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4 Citations (Scopus)
141 Downloads (Pure)

Abstract

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)
PublisherIEEE
Pages256-260
ISBN (Electronic)978-0-9928626-7-1
ISBN (Print)978-1-5386-0751-0
DOIs
Publication statusPublished - 2017
MoE publication typeA4 Article in a conference publication
EventEuropean Signal Processing Conference - Kos Island, Greece, Kos, Greece
Duration: 28 Aug 20172 Sep 2017
Conference number: 25
https://www.eusipco2017.org
https://www.eusipco2017.org/

Publication series

NameEuropean Signal Processing Conference
ISSN (Electronic)2076-1465

Conference

ConferenceEuropean Signal Processing Conference
Abbreviated titleEUSIPCO
CountryGreece
CityKos
Period28/08/201702/09/2017
Internet address

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