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.

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用于呼吸率监测的 RSS 模型

基于接收信号强度的呼吸率监测正在成为一种替代的非接触式技术。这些系统利用短距离商品无线设备的无线电测量，这些设备因人的吸气和呼气运动而变化。使用这样的测量值估计呼吸率的成功取决于信噪比，它会随着人的特性和测量系统而改变。迄今为止，还没有提出可以评估不同部署或系统配置以成功估计呼吸率的模型。本文介绍了一种用于呼吸率监测的接收信号强度模型。结果表明，线性和对数尺度的测量具有相同的函数形式，并且在两种情况下都可以使用相同的估计技术。该模型进行了数值和经验评估，并深入讨论了其特性。使用三个估计器的性能在不同的信噪比条件下验证最重要的模型含义：批量频率估计器、递归贝叶斯估计器和基于模型的估计器。结果与发现一致，它们意味着不同的估计量在不同的信噪比方案中是有利的。