From Sensing to Intelligence: How AI Improves mmWave Radar Capabilities for Contactless Health Monitoring

Millimeter-wave (mmWave) radar is becoming an important tool for contactless health monitoring because it can sense very small chest motions while preserving privacy by avoiding visual imagery. Existing surveys on radar- or RF-based vital sign monitoring either focus mainly on classical radar architectures and signal processing, provide broad RF sensing overviews in which mmWave healthcare is treated only briefly, or catalog machine learning models without clearly linking them to mmWave propagation, hardware constraints, datasets, and clinical evaluation practices. Because of these gaps, we believe the existing surveys do not provide a holistic, accurate picture of this technology. To address this, we present a comprehensive survey of AI-enabled mmWave radar for contactless health monitoring, covering the literature from 2015 to 2025. The objective of this work is to provide a clear, top–down understanding of the full sensing and inference pipeline by connecting the physical foundations of mmWave propagation and frequency-modulated continuous-wave (FMCW) radar modeling with modern AI-based algorithms. We first summarize mmWave propagation, FMCW waveform, and array design, and micromotion modeling, with emphasis on design choices that affect vital sign accuracy and robustness. To do this, we introduce a unified physics-to-intelligence framework that connects sensing configurations, subject scenarios, signal-processing and feature-representation pipelines, and the evolution of AI algorithms such as CNNs, LSTMs, transformers, self-supervised learning, and physics-guided networks. In parallel, we consolidate the scarce public mmWave FMCW datasets, together with windowing protocols and evaluation metrics, and highlight how limited dataset availability and heterogeneous benchmark practices continue to prevent fair comparison, reproducibility, and clinical translation. Building on this view, we discuss major challenges such as domain generalization, motion and interference, model interpretability and trust, privacy, multimodal fusion, and edge deployment, and we outline a practical roadmap for designing mmWave health monitoring systems that are robust across environments, efficient on embedded platforms, and aligned with clinical workflows. The survey is intended to serve researchers working at the intersection of wireless communications, sensing, and AI, both as a reference and a design guide for next-generation contactless health-monitoring applications.