RNN-based Phase Unwrapping for Enabling Vital Parameter Monitoring with FMCW Radars

Abstract

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.