RSS Models for Respiration Rate Monitoring

TL;DR

This paper introduces a model for RSS-based respiration rate monitoring, enabling evaluation of system configurations and deployment effectiveness across varying signal-to-noise ratios, validated with multiple estimators.

Contribution

A novel received signal strength model for respiration monitoring that applies to both linear and logarithmic scales, facilitating system evaluation and comparison.

Findings

Model works in both linear and logarithmic scales

Estimator performance varies with signal-to-noise ratio

Different estimators are optimal in different noise regimes

Abstract

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.