Zhang Xiaoyu, Liu Chuhui, Cheng Yanda, Li Zhengxiong, Xu Chenhan, Huang Chuqin, Zhan Ye, Bo Wei, Xia Jun, Xu Wenyao
Department of Computer Science and Engineering, State University of New York at Buffalo, Amherst, NY 14068, USA.
Department of Biomedical Engineering, State University of New York at Buffalo, Amherst, NY 14068, USA.
Sensors (Basel). 2025 Jun 13;25(12):3706. doi: 10.3390/s25123706.
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.
毫米波（mmWave）传感已成为一种用于非接触式健康监测的有前途的技术，具有高空间分辨率、材料敏感性以及与无线平台的集成潜力。虽然先前的工作主要集中在特定应用或信号处理方法上，但对于毫米波信号如何映射到临床相关生物标志物仍缺乏统一的理解。本综述对基于毫米波的医学传感系统进行了全面的堆栈式回顾，涵盖信号采集、物理特征提取、建模策略以及潜在的医学和医疗保健用途。我们引入了一种分类法，将诸如运动、材料特性和结构等低层次毫米波信号特征与包括呼吸模式、心率、组织水合作用和步态等高层次生物医学标志物解耦。然后，我们对从物理驱动的分析模型到机器学习技术等实现这种映射的建模方法进行分类和对比。此外，我们分析了在生命体征监测、心血管评估、伤口评估和神经运动障碍方面的代表性研究。通过将无线传感与医学解读联系起来，这项工作为设计下一代毫米波健康监测系统提供了结构化参考。我们通过讨论开放挑战来得出结论，包括模型可解释性、临床验证和多模态集成。
