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Two vital signs including heartbeat and respiratory rate are monitored in this work under two constraint situations; namely noise disturbance and intermittent observations. The existing scheme for finding, measuring and monitoring vital signs was Fourier Transform which could not deal with non-stationary process. As an alternative, the Wavelet Transform is used in this work which is equally applicable to both stationary and non-stationary processes. Additionally, the loss of output data may result in crucial implications in observing vital signs. Formerly, only un-interrupted data has been amalgamated in tracing vital signs. A novel adaptive ARMA-based scheme is proposed to obtain optimum estimated results in the presence of the above two critical scenarios. Simulation results obtained on real (practical) data show that the ARMA-based model produces similar vital signs as shown by clean and un-distorted data. It is shown that the proposed ARMA-based algorithm improves the breathing rate accuracy by 0.3% and heart rate accuracy by 2.5% as compared to the existing AR-based vital signal reconstruction algorithm.

Handling Radar Cross-Section Performance in Monitoring Vital Signs Under Constraint Conditions