SCH: Wireless sensing of biomarkers using intelligent surfaces for smart homes and sleep medicine - The ability to retrieve biomarkers like heart and respiratory rates without disrupting a user's typical sleep environment can revolutionize sleep medicine, smart healthcare, independent living, and sleep health monitoring of cancer patients. Sleep health is closely tied to cancer risk, cancer prognosis, and health-related quality of life of cancer survivors. This need has driven an ever-growing interest in wireless remote sensing of biomarkers using WiFi signals or radars to monitor cardio-respiratory signals and thereby assess sleep quality, timing and duration. However, these technologies typically use a small number of antennas, limiting their spatial resolution. As a result, their performance can drastically deteriorate when a user moves, changes orientation, or when multiple people are present. This leaves wearable sensors as the main available non- intrusive privacy-preserving technology for sleep monitoring, but they require frequent user intervention and charging. To address these shortcomings, we will develop a smart wireless environment using multi-band reconfigurable intelligent surfaces (RISs). These surfaces are low cost, low power, and can arbitrarily redirect wireless signals. They are projected to play critical roles in next-generation wireless communication networks, ensuring widespread deployment. Hence, the proposed system aligns synergistically with the rollout of future wireless communication systems. By utilizing microwave RISs, we will retrieve users' low- resolution RF reflectivity maps by developing a comprehensive physics-based model for RIS-based computational imaging. This RF image is then interpreted using a novel multi-modal learning algorithm to detect and track regions of interest (e.g., the torso). A high-resolution wideband millimeter RIS is then used to focus on the user’s torso to retrieve breathing and heart rate with high spatial resolution. We will design and experimentally verify the novel hardware and processing algorithms required to implement such a smart wireless integrated monitoring (SWIM) system. We will demonstrate how multiple low-cost sensors in the form of multiband, high-resolution intelligent surfaces and off-the-shelf radars can collaborate and robustly retrieve critical cardiorespiratory biomarkers used to measure sleep without disrupting their daily routines. RELEVANCE (See instructions): The ability to monitor sleep by measuring breathing and heart rate using a smart system that uses wireless waves from the walls of the rooms can revolutionize sleep monitoring in people with cancer. Poor sleep may lead to cancer, and cancer treatment outcomes may be influenced by sleep including quality of life in survivors. We will build and test a smart wireless system called SWIM to monitor sleep easily. Such an approach will enable future sleep research for cancer prevention and treatment.
