A survey on machine learning approaches for vital sign monitoring using radar

Abstract

The integration of machine learning methodologies with radar-based vital sign monitoring represents a significant advancement in non-contact healthcare surveillance systems. This systematic literature review synthesizes and critically analyzes research from 2020 to 2025, addressing substantive theoretical and methodological gaps in extant literature. Our comprehensive taxonomic classification of machine learning paradigms employed in this domain elucidates the progressive refinement from conventional algorithmic approaches to sophisticated deep learning architectures, with particular emphasis on hybrid neural network configurations optimized for physiological signal extraction in non-stationary environments. Methodologically, this survey contributes a rigorous evaluation framework comprising standardized assessment protocols, quantifiable performance metrics, and cross-validation methodologies—elements conspicuously absent in previous reviews. Empirical analysis demonstrates substantial correlations between dataset demographic characteristics and algorithmic generalizability, with heterogeneous participant cohorts yielding markedly enhanced performance across cardiac, respiratory, and hemodynamic parameter estimation tasks. The review delineates four distinct developmental phases in the field’s chronological evolution and provides analytical insight into persistent technical challenges: motion artifact compensation, multi-subject disambiguation, and the translation of laboratory efficacy to clinical utility. This comprehensive examination of computational approaches for radar-based vital sign monitoring establishes a theoretical foundation and methodological framework to guide future research towards physiologically robust and clinically viable implementations.