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Millimeter-wave (mmWave) sensing has emerged as a promising technology for non-contact health monitoring, offering high spatial resolution, material sensitivity, and integration potential with wireless platforms. While prior work has focused on specific applications or signal processing methods, a unified understanding of how mmWave signals map to clinically relevant biomarkers remains lacking. This survey presents a full-stack review of mmWave-based medical sensing systems, encompassing signal acquisition, physical feature extraction, modeling strategies, and potential medical and healthcare uses. We introduce a taxonomy that decouples low-level mmWave signal features—such as motion, material property, and structure—from high-level biomedical biomarkers, including respiration pattern, heart rate, tissue hydration, and gait. We then classify and contrast the modeling approaches—ranging from physics-driven analytical models to machine learning techniques—that enable this mapping. Furthermore, we analyze representative studies across vital signs monitoring, cardiovascular assessment, wound evaluation, and neuro-motor disorders. By bridging wireless sensing and medical interpretation, this work offers a structured reference for designing next-generation mmWave health monitoring systems. We conclude by discussing open challenges, including model interpretability, clinical validation, and multimodal integration. 

As the drone becomes widespread in numerous crucial applications with many powerful functionalities (e.g., reconnaissance and mechanical trigger), there are increasing cases related to misused drones for unethical even criminal activities. Therefore, it is of paramount importance to identify these malicious drones and track their origins using digital forensics. Traditional drone identification techniques for forensics (e.g., RF communication, ID landmarks using a camera, etc.) require high compliance of drones. However, malicious drones will not cooperate or even spoof these identification techniques. Therefore, we present an exploration for a reliable and passive identification approach based on unique hardware traits in drones directly (e.g., analogous to the fingerprint and iris in humans) for forensics purposes. Specifically, we investigate and model the behavior of the parasitic electronic elements under RF interrogation, a particular passive parasitic response modulated by an electronic system on drones, which is distinctive and unlikely to counterfeit. Based on this theory, we design and implement DroneTrace, an end-to-end reliable and passive identification system toward digital drone forensics. DroneTrace comprises a cost-effective millimeter-wave (mmWave) probe, a software framework to extract and process parasitic responses, and a customized deep neural network (DNN)-based algorithm to analyze and identify drones. We evaluate the performance of DroneTrace with 36 commodity drones. Results show that DroneTrace can identify drones with the accuracy of over 99% and an equal error rate (EER) of 0.009, under a 0.1-second sensing time budget. Moreover, we test the reliability, robustness, and performance variation under a set of real-world circumstances, where DroneTrace maintains accuracy of over 98%. DroneTrace is resilient to various attacks and maintains functionality. At its best, DroneTrace has the capacity to identify individual drones at the scale of 104 with less than 5% error.