Numerical Examination of RIS-Enabled Microwave Imaging for Sleep Monitoring

This paper introduces a reconfigurable intelligent surface (RIS)-enabled framework for microwave imaging aimed at non-invasive human monitoring in indoor environments. Our primary objective is to generate a representative reflectivity map of a resting or sleeping individual. This imaging can facilitate tracking of movements and biomarkers, such as breathing, during rest or sleep. Achieving this challenging task necessitates high signal strength, robustness against clutter, and minimal measurements to ensure fast data acquisition. To address these requirements, the proposed system utilizes programmable RIS configurations to manipulate the electromagnetic field distribution within the region of interest (ROI). In contrast to traditional methods, we combine radar-coincidence imaging (RCI) with computational imaging techniques. The former method yields high signal strength and facilitates clutter rejection, while the latter reduces the number of required measurements. Numerical simulations using a humanoid demonstrate the feasibility of reconstructing key features. The low number of measurements paves the way for fast image reconstruction and movement detection. Additionally, a comparative study with conventional random illumination is conducted, demonstrating improved reconstruction outcomes. These findings underscore the potential of RIS-enabled imaging for non-contact biomarker monitoring applications.