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.


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    Titel :

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


    Beteiligte:
    Lin, Guiping (Autor:in) / Men, Jing (Autor:in) / Zhang, Tingting (Autor:in)


    Erscheinungsdatum :

    07.10.2024


    Format / Umfang :

    11430878 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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