Vital Signs Estimation Scheme Based on Autocorrelation and Variational Mode Decomposition Using Ultra-Wide Band Radar

Abstract

Vital sign monitoring is essential in the areas of smart healthcare and personal wellness. Radar based vital signs detection provides a noninvasive way to extract vital signs indicators, such as heartbeat rate and respiratory rate without any privacy concerns. However, the higher-order harmonics of respiratory signals and heartbeat signals may exhibit varying degrees of frequency overlap. Although the signal energy of the higher-order harmonics of respiratory signals is negligible compared to the signal energy of the respiratory signal itself, it can still completely mask the signal energy of the heartbeat signal, so that the corresponding multiple frequency of the respiratory rate will be highly likely to be identified as the heartbeat rate, resulting in a deviation between the estimated and the actual heartbeat rate. In this paper, we proposed a vital signs extraction scheme based on the autocorrelation and variational mode decomposition algorithm to acquire breath rate and heartbeat rate with removing the effect of higher-order harmonics of respiratory signals. Finally, we achieved a mean heartbeat rate accuracy of 89.7% in heartbeat rate estimation and a mean absolute error per minutes of 1.7 in respiratory rate estimation.