Radar systems have been widely explored as a monitoring tool able to assess the subject’s vital signs remotely. However, their implementation in real application scenarios is not straightforward. Received signals encompass parasitic reflections that occur in the monitoring environment. Generally, those parasitic components, often treated as a complex DC (CDC) offsets, must be removed in order to correctly extract the bio-signals information. Fitting methods can be used, but their implementation were revealed to be challenging when bio-signals are weak or when these parasitic reflections arise from non-static targets, changing the CDC offset properties over time. In this work, we propose a dynamic digital signal processing algorithm to extract the vital signs from radar systems. This algorithm includes a novel arc fitting method to estimate the CDC offsets on the received signal. The method revealed being robust to weaker signals, presenting a success rate of 95%, irrespective of the considered monitoring conditions. Furthermore, the proposed algorithm is able to adapt to slow changes in the propagation environment.
Doppler-based radar systems have been seen as a promising tool to assess vital signs, since they are capable to monitor the respiratory and cardiac signal remotely, by measuring the chest-wall displacement. However, due to the spectral overlap of these signals, their proper separation is a challenging task. In this paper, we demonstrate the effectiveness of using Discrete Wavelet Transform in the cardiac signal extraction, by comparing this method with other approaches widely used in literature, namely a standalone Band-Pass Filtering, the Ensembled Empirical Mode Decomposition, the Continuous Wavelet Transform and the Wavelet Packet Decomposition. The comparison metrics were defined taking into consideration the heart rate computation accuracy, and also the peak detection consistency to further evaluate the Heart Rate Variability. The efficiency of those methods is also tested considering real application scenarios, characterized by non-controlled monitoring environment conditions and the ability to equally assess the vital signs of different subjects, regardless their physical stature.
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