Cardiovascular disease remains the leading cause of mortality worldwide, yet effective detection, monitoring, and timely intervention can significantly improve treatment outcomes. Early identification of conditions such as arrhythmias, valvular disorders, heart failure, and hypertension is crucial for enhancing patient outcomes. However, traditional detection and diagnosis methods are often confined to in-clinic examinations, which not only incur high costs, making them inaccessible for many, but also provide only episodic insights, potentially missing symptomatic periods. A paradigm shift to continuous cardiovascular monitoring could transform healthcare delivery by offering a continuous, accessible, and convenient analysis of cardiovascular health. Near-field radiofrequency (RF) sensing technology can enable this paradigm shift, by offering continuous, real-time heart and lung motion monitoring without direct skin contact, allowing for monitoring over clothing or via furniture integration. This non-contact capability sets it apart from conventional sensing methods like electrocardiography, ultrasound/echocardiography, or photoplethysmography. This thesis explores the physiological underpinnings that correlate cardiac motion with near-field RF sensing signals, the development of specialized algorithms for processing this distinctive data, and its clinical implications for convenient and continuous monitoring. The two main objectives are explored: firstly, establishing a link between the cardiac near-field RF sensing signal and cardiac physiology through comparisons with high-fidelity ground truth sensors; and secondly, utilizing this physiological understanding to explore new cardiac applications for near-field RF sensors. We determined that the near-field RF sensing signal, through specialized processing and analysis, exhibits a non-linear correlation with ventricular volume. This interpretation paves the way for advanced volumetric and vascular monitoring application to advance the capabilities of cardiac near-field RF sensing.