RSS Models for Respiration Rate Monitoring

Research output: Contribution to journalArticleScientificpeer-review


Research units

  • University of Utah
  • Xandem Technology LLC


Received signal strength based respiration rate monitoring is emerging as an alternative non-contact technology. These systems make use of the radio measurements of short-range commodity wireless devices, which vary due to the inhalation and exhalation motion of a person. The success of respiration rate estimation using such measurements depends on the signal-to-noise ratio, which alters with properties of the person and with the measurement system. To date, no model has been presented that allows evaluation of different deployments or system configurations for successful breathing rate estimation. In this paper, a received signal strength model for respiration rate monitoring is introduced. It is shown that measurements in linear and logarithmic scale have the same functional form, and the same estimation techniques can be used in both cases. The model is numerically and empirically evaluated, and its properties are discussed in depth. The most important model implications are validated under varying signal-to-noise ratio conditions using the performances of three estimators: batch frequency estimator, recursive Bayesian estimator, and model-based estimator. The results are in coherence with the findings, and they imply that different estimators are advantageous in different signal-to-noise ratio regimes.


Original languageEnglish
Article number8634923
Pages (from-to)680-696
Number of pages17
JournalIEEE Transactions on Mobile Computing
Issue number3
Early online date2019
Publication statusPublished - 1 Mar 2020
MoE publication typeA1 Journal article-refereed

    Research areas

  • Radio frequency propagation, Received signal strenght measurement, Respiration rate monitoring, Frequency estimation

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