Zhou, Jianlin2024-01-312023-05Zhou_cornellgrad_0058F_13523http://dissertations.umi.com/cornellgrad:13523https://hdl.handle.net/1813/114185The COVID-19 pandemic brings us a critical reminder: our medical system is weak under the attack of a highly contagious disease, and medical resources are too limited to provide timely diagnosis and treatment for a large patient population. During the pandemic, the demand for home and wearable medical sensors, such as antigen sensors and finger pulse oximeters, had explosive growth. Furthermore, with the help of artificial intelligence, early detection and health status become feasible if sensors can provide continuous vital signs or bio-marker data. This is a potential indicator that with the maturing of digital medical sensors, hospital-centric disease diagnosis and prognosis can gradually migrate to home in daily life. Electromagnetic (EM) waves of different frequency bands have long been used in medical settings for imaging and diagnostic purposes, such as magnetic resonance imaging (MRI), Computer tomography (CT) scan, and microwave tomography. Advanced research in EM technology has been a key driver in pushing the frontier of medical and biological research in which the interaction of EM fields with biological systems has played an essential role. However, the high cost, system complexity, large size, and health hazards of radiation exposure make these state-of-the-art medical equipment not feasible for daily at-home uses and continuous health monitoring. In the past four decades, microwave sensing has been enhanced by significant hardware advancement and miniaturization. Researchers have applied various radio-frequency (RF) technologies, such as far-field radar and impedance tomography, in health monitoring and have significantly improved vital-sign monitoring in humans and animals. The change of target geometry and material property are represented by the backscattered radio wave features, such as the frequency, magnitude, phase, and radar cross-section (RCS). However, while enabling fully non-contact operation, most far-field radar sensors are monostatic and often require a direct line-of-sight (LOS) with the target\textquotesingle s moving surface. Furthermore, most of these far-field radar systems have large sizes and high power consumption, which limit their usage for mobile applications. Unlike far-field radar sensors, wearable RF sensors, whether by active units or passive tags, allow subjects to move freely during monitoring and couple more EM energy directly inside the body in the local near-field zone. This work focuses on this near-field RF sensing method to accurately measure physiological organ and tissue motion and contributes two essential aspects of design optimization and wearable applications: 1) In the near-field RF sensing theory, we first analyze the near-field radio and dielectric object interaction mechanism and propose a backscatter field model for near-field RF sensors, which considers the scattering field, permittivity, object size, sensor positions, and carrier frequency. Second, we analyze the composition of the Rx radio signal and explicitly explore how the time-invariant component affects the morphology of the complex signal's magnitude and phase representation. To optimize signal features and regulate the waveform morphology for consistent signal interpretation, we propose a complex vector injection algorithm. 2) In the near-field RF sensing application, we introduce the design of low-power wearable near-field RF sensor platforms and their applications on human and animal vital signs sensing.endissertation or thesishttps://doi.org/10.7298/j4zr-4m95