Abstract
Non-contact vital sign monitoring has several advantages over conventional methods, such as comfort, unobtrusiveness, and a reduced risk of infection transmission. Low-power millimeter-wave (mmWave) radars offer a promising solution for contactless vital sign measurement in embedded, battery-operated devices. Reduced power of these sensor systems leads to challenges associated with precise and reliable vital sign monitoring, particularly with regard to heart rate (HR) measurement. This work investigates adaptations of standard signal processing steps to extract HR and respiration rate (RR) from the raw data of a Frequency Modulated Continuous Wave (FMCW) radar signal suitable for low-power operation. By using less than 100 kB of RAM it could fit into a typical Bluetooth low-energy module. Additionally, we evaluate the performance of our algorithms on 10 people including different body types, physical conditions and sex achieving a mean absolute error in HR estimation of two beats per minute and below one breath per minute error for the RR.
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