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.
@cite_30: A radio transceiver normally provides received signal strength (RSS) quantized with 1 dB or higher step size. Currently, we know of no application which has demonstrated a need for sub-dB RSS estimates. In this paper, we demonstrate the need for, and benefits of, greater resolution in RSS for breathing rate monitoring and gesture recognition. Measuring RSS requires orders of magnitude less bandwidth than measuring OFDM channel state information (CSI) or frequency modulated carrier wave (FMCW) channel delay. We have designed a prototype with an off-the-shelf low-power transceiver and a processor to achieve an RSS estimate with a median error of 0.013 dB. We experimentally verify its performance in non-contact breathing monitoring and gesture recognition. We demonstrate that simply decreasing the step size of RSS lower than 1 dB can enable significant benefits, enabling extremely low bandwidth RF sensing systems. Results indicate that RFIC designers could enable significant gains for RF sensing applications with four more bits of RSS quantization.
@cite_4: Estimation of the parameters of a single-frequency complex tone from a finite number of noisy discrete-time observations is discussed. The appropriate Cramer-Rao bounds and maximum-likelihood (MI.) estimation algorithms are derived. Some properties of the ML estimators are proved. The relationship of ML estimation to the discrete Fourier transform is exploited to obtain practical algorithms. The threshold effect of one algorithm is analyzed and compared to simulation results. Other simulation results verify other aspects of the analysis.
@cite_8: Recent research has demonstrated the feasibility of detecting human respiration rate non-intrusively leveraging commodity WiFi devices. However, is it always possible to sense human respiration no matter where the subject stays and faces? What affects human respiration sensing and what's the theory behind? In this paper, we first introduce the Fresnel model in free space, then verify the Fresnel model for WiFi radio propagation in indoor environment. Leveraging the Fresnel model and WiFi radio propagation properties derived, we investigate the impact of human respiration on the receiving RF signals and develop the theory to relate one's breathing depth, location and orientation to the detectability of respiration. With the developed theory, not only when and why human respiration is detectable using WiFi devices become clear, it also sheds lights on understanding the physical limit and foundation of WiFi-based sensing systems. Intensive evaluations validate the developed theory and case studies demonstrate how to apply the theory to the respiration monitoring system design.
@cite_15: Respiratory rate is an important vital sign that can indicate progression of illness but to also predict rapid decline in health. For the purpose, non-contact monitoring systems are becoming more popular due to the self-evident increase in patient comfort. As a cost effective solution for non-invasive breathing monitoring, utilizing the received signal strength measurements of inexpensive transceivers has been proposed. However, the applicability of the available solutions is limited since they rely on numerous sensors. In this work, considerable improvement is made, and a respiratory rate monitoring system based on a single commercial off-the-shelf transmitter-receiver pair is presented. Methods that enable estimation and enhance the accuracy are presented and their effects are evaluated. Moreover, it is empirically demonstrated that the performance of the system is comparable to the accuracy of a high-end device for 3-4 orders of magnitude less price; achieving mean absolute error of 0.12 breaths per minute in the most realistic scenario of the experiments.
@cite_25: A growing set of environmental sensing applications use received signal strength measurements of a static wireless network for unobtrusive monitoring purposes. The success of these systems, which typically process low-amplitude signals, depend strongly on the distribution of the measurements when there are no changes in the channel. In this letter, a statistical model for signal strength measurements acquired when the environment is static is studied. As previously empirically verified, it is shown that the measurements have log-normal distribution even in idealistic environments, which cannot be explained using log-normal shadow fading arguments. Quantization and round-off errors induced by different measurement system components are also considered, and their impact are analyzed. As a result, it is shown that the logarithmic received signal strength measurements under static channel conditions are samples from stationary Gaussian process independent of the environment.
In this work, we model the RSS of narrowband communication systems for small periodic perturbations with unknown frequency, direction, and amplitude. It is shown that, similarly to signals in radar systems, the RF signal frequency modulate the periodic vital signals. The resultant model shows explicit relation between the respiration signal and initial position of the person, her orientation with respect to link-line, amplitude of the breathing, and the wavelength of the communication system. The SNR depends on these parameters as well as the noise power which dictates the performance of single tone parameter estimation techniques @cite_4 . This result is coherent with empirical results of @cite_30 , real-time spectrum analyzer measurements in @cite_15 , and findings of @cite_8 . The model also shows various interesting situations arising in practical deployments including the measurements showing only odd or even harmonics. It is further shown that the logarithmic transformation taking place in typical RSS measurement systems @cite_25 does not change the signal type. Therefore, the developed model can be used for evaluating various deployment conditions that enable successful breathing rate monitoring.