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Currently, wired respiratory rate sensors tether patients to a location and can potentially obscure their body from medical staff. In addition, current wired respiratory rate sensors are either inaccurate or invasive. Spurred by these deficiencies, we have developed the Bellyband, a less invasive smart garment sensor, which uses wireless, passive Radio Frequency Identification (RFID) to detect bio-signals. Though the Bellyband solves many physical problems, it creates a signal processing challenge, due to its noisy, quantized signal. Here, we present an algorithm by which to estimate respiratory rate from the Bellyband. The algorithm uses an adaptively parameterized Savitzky-Golay (SG) filter to smooth the signal. The adaptive parameterization enables the algorithm to be effective on a wide range of respiratory frequencies, even when the frequencies change sharply. Further, the algorithm is three times faster and three times more accurate than the current Bellyband respiratory rate detection algorithm and is able to run in real time. Using an off-the-shelf respiratory monitor and metronome-synchronized breathing, we gathered 25 sets of data and tested the algorithm against these trials. The algorithm’s respiratory rate estimates diverged from ground truth by an average Root Mean Square Error (RMSE) of 4.1 breaths per minute (BPM) over all 25 trials. Further, preliminary results suggest that the algorithm could be made as or more accurate than widely used algorithms that detect the respiratory rate of non-ventilated patients using data from an Electrocardiogram (ECG) or Impedance Plethysmography (IP). 

Traditional approaches to experimental characterization of wireless communication systems typically involves highly specialized and small-scale experiments to examine narrow aspects of each of these applications. We present the Grid SDR testbed, a unified experimental framework to rapidly prototype and evaluate these diverse systems using: (i) field measurements to evaluate real time transceiver and channel-specific effects and (ii) network emulation to evaluate systems at a large scale with controllable and repeatable channels. We present the hardware and software architecture for our testbed, and describe how it being used for research and education. Specifically, we show experimental network layer metrics in different application domains, and discuss future opportunities using this unique experimental capability.