(Wydawnictwo Politechniki Łódzkiej, 2023) Łuczak, Piotr; Hausman, Sławomir; Ślot, Krzysztof
Application of radar technology enables remote breathing and
heart rate monitoring by analyzing motion waveforms, which are reconstructed
from phase signals extracted from radar-delivered data. However,
nonlinear deformations introduced by phase recovery procedure make accurate
motion reconstruction highly challenging, especially for millimeter-long
waves that are commonly generated by state-of-the-art radar devices. In the
presented paper we show that a GRU-based neural predictor is capable of
correct phase unwrapping under presence of noise (originating e.g. from
random subject’s movements), enabling vital parameter monitoring in realistic
scenarios, which cannot be accomplished using standard approaches.
