[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124246-en":3,"doc-seo-124246-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124246,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","A survey on machine learning approaches for vital sign monitoring using radar","The integration of machine learning with radar-based vital sign monitoring enables non-contact healthcare surveillance with improved practicality and privacy. This systematic literature review synthesizes and critically analyzes studies from 2020 to 2025, targeting key theoretical and methodological gaps in existing work. A comprehensive taxonomy clarifies how approaches evolve from conventional algorithms to advanced deep learning, highlighting hybrid neural networks for extracting physiological signals in non-stationary conditions. The review proposes standardized evaluation protocols and demonstrates links between dataset demographics and algorithm generalizability across cardiac, respiratory, and hemodynamic tasks.","Journal Pre-proof  \nA survey on machine learning approaches for vital sign monitoring using radar  \nMohammad Hossein Shirazi, Sira Yongchareon, Anuradha Singh, Julia Ma  \nPII: S0263-2241(25)01066-8  \nDOI: [https://doi.org/10.1016/j.measurement.2025.117707](https://doi.org/10.1016/j.measurement.2025.117707)  \nReference: MEASUR 117707  \nTo appear in: Measurement  \nReceived date : 7 January 2025  \nRevised date : 23 April 2025  \nAccepted date : 26 April 2025  \nPlease cite this article as: M.H. Shirazi, S. Yongchareon, A. Singh et al., A survey on machine learning approaches for vital sign monitoring using radar, Measurement (2025), doi:  \n[https://doi.org/10.1016/j.measurement.2025.117707](https://doi.org/10.1016/j.measurement.2025.117707) .  \nThis is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.  \n© 2025 Published by Elsevier Ltd.  \nJournal Pre-proof  \nHighlights  \nA Survey on Machine Learning Approaches for Vital Sign Monitoring Using Radar  \nMohammad Hossein Shirazi*, Sira Yongchareon, Anuradha Singh, Julia Ma  \n• Systematic analysis of machine learning architectures for radar-based vital sign monitoring from 2020-2025 .  \n• Quantitative assessment of laboratory-to-real-world performance gaps across monitoring paradigms.  \n• Demographic impact analysis revealing how subject diversity affects algorithm generalizability.  \n• Comparative evaluation of supervised versus unsupervised learning approaches for different monitoring scenarios.  \n• Standardized evaluation protocols for consistent cross-study comparisons in vital sign monitoring.  \nJournal Pre-proof  \nA Survey on Machine Learning Approaches for Vital Sign Monitoring Using Radar  \nMohammad Hossein Shirazi*a , Sira Yongchareona , Anuradha Singha , Julia  \nMaa  \na Auckland University of Technology, ECMS, Auckland, New Zealand  \nAbstract  \nThe 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 f","cbCaibJgAlEnDoeG","https://ap.wps.com/l/cbCaibJgAlEnDoeG","pdf",3140426,1,81,"English","en",105,"# Introduction\n## Vital signs and monitoring needs\n## Sensor technologies and contactless radar\n# Radar-based vital sign monitoring\n## Privacy and environmental robustness","[{\"question\":\"What is the document’s main focus?\",\"answer\":\"It focuses on surveying machine learning approaches for vital sign monitoring using radar, covering theoretical and methodological gaps across 2020–2025 research.\"},{\"question\":\"Which vital signs and tasks are emphasized?\",\"answer\":\"The document emphasizes heart rate, respiratory rate, and blood pressure, including tasks such as cardiac, respiratory, and hemodynamic parameter estimation.\"},{\"question\":\"What does the survey contribute methodologically?\",\"answer\":\"It introduces a structured evaluation framework with standardized assessment protocols, performance metrics, and cross-validation methods to support consistent comparisons across studies.\"}]","A survey on machine learning approaches for vital sign monitoring using radar | 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