  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 implications of the model 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.
