Adaptive VMD-Based Vital Signs Recognition Using Maximum Cross-Correlation with IR-UWB Radar

The Anxiety and Depression Association of America reports that about 40 million Americans suffer from anxiety disorders. Prolonged sitting and high-stress work can exacerbate cardiovascular disease, a leading cause of death worldwide in recent decades. Therefore, monitoring the vital signs of working or studying individuals is crucial to preventing sudden death from cardiovascular disease. Non-invasive sensing is crucial due to the challenges posed by complex wired connections or frequent charging. In this paper, we monitor vital signs using impulse ultra-wideband radar. The separation of respiratory rate and heartbeat signals is achieved adaptively through the variational mode decomposition algorithm, combined with maximum cross-correlation and Butterworth filtering. In a 6-hour experiment involving participants, the proposed method achieves a cumulative distribution function error within 1 for 93% of respiratory rate estimations, and a median error in heartbeat estimation ranging between 5 and 6. This validation strongly supports advancements in future vital signs monitoring technology.