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.
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.
@cite_30 @cite_4 @cite_8 @cite_15 @cite_25
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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. It has been shown that remote monitoring of pulmonary activity can be achieved using ultra-wideband (UWB) systems, which shows promise in home healthcare, rescue, and security applications. In this paper, we first present a multi-ray propagation model for UWB signal, which is traveling through the human thorax and is reflected on the air dry-skin fat muscle interfaces. A geometry-based statistical channel model is then developed for simulating the reception of UWB signals in the indoor propagation environment. This model enables replication of time-varying multipath profiles due to the displacement of a human chest. Subsequently, a UWB distributed cognitive radar system (UWB-DCRS) is developed for the robust detection of chest cavity motion and the accurate estimation of respiration rate. The analytical framework can serve as a basis in the planning and evaluation of future measurement programs. We also provide a case study on how the antenna beamwidth affects the estimation of respiration rate based on the proposed propagation models and system architecture. Among the various ranging techniques, Radio Signal Strength (RSS) based approaches attract intensive research interests because of its low cost and wide applicability. RSS-based ranging is apt to be affected by the multipath phenomenon which allows the radio signals to reach the destination through multiple propagation paths. To address this issue, previous works try to profile the environment and refer this profile in run-time. In practical dynamic environments, however, the profile frequently changes and the painful retraining is needed. Rather than such static ways of profiling the environments, in this paper, we try to accommodate the environmental dynamics automatically in real-time. The key observation is that given a pair of nodes, the RSS at different spectrum channels will be different. This difference carries the valuable phase information of the radio signals. By analyzing these RSS values, we are able to identify the amplitude of signals solely from the Line-of-Sight (LOS) path. This LOS amplitude is a simple function of the path length (the physical distance). We find that the analysis is a typical non-linear curvature fitting problem that has no general routing algorithms. We prove this problem format is ill-conditioned which has no stable and trustable solutions. To deal with this issue, we further explore the practical considerations for the problem and reform it to a greatly improved conditioning shape. We solve the problem by numerical iterations and implement these ideas in a real-time indoor tracking system called MuD. MuD employs only three TelosB nodes as anchors. The experiment results show that in a dynamic environment where five people move around, the averaged localization error is 1 meter. Compared with the traditional RSS-based approaches in dynamic environment, the accuracy improves up to 10 times. Monitoring breathing rates and patterns helps in the diagnosis and potential avoidance of various health problems. Current solutions for respiratory monitoring, however, are usually invasive and or limited to medical facilities. In this paper, we propose a novel respiratory monitoring system, UbiBreathe, based on ubiquitous off-the-shelf WiFi-enabled devices. Our experiments show that the received signal strength (RSS) at a WiFi-enabled device held on a person's chest is affected by the breathing process. This effect extends to scenarios when the person is situated on the line-of-sight (LOS) between the access point and the device, even without holding it. UbiBreathe leverages these changes in the WiFi RSS patterns to enable ubiquitous non-invasive respiratory rate estimation, as well as apnea detection. We propose the full architecture and design for UbiBreathe, incorporating various modules that help reliably extract the hidden breathing signal from a noisy WiFi RSS. The system handles various challenges such as noise elimination, interfering humans, sudden user movements, as well as detecting abnormal breathing situations. Our implementation of UbiBreathe using off-the-shelf devices in a wide range of environmental conditions shows that it can estimate different breathing rates with less than 1 breaths per minute (bpm) error. In addition, UbiBreathe can detect apnea with more than 96 accuracy in both the device-on-chest and hands-free scenarios. This highlights its suitability for a new class of anywhere respiratory monitoring.