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State-of-the-Art Technologies and Emerging Trends in Radar-Based vital signs Sensing: A Review

  • Tongyang Yuan
  • , Youyu Kuo
  • , Aifeng Ren
  • , Runqi Huo
  • , Qing Sun
  • , Adnan Zahid

Research output: Contribution to journal › Article › peer-review

41 Downloads (Pure)

Abstract

Radar-based non-contact vital signs sensing has emerged as a pivotal research direction in biomedical engineering and beyond, providing a privacy-preserving and unobtrusive alternative to conventional contact sensors and vision-based systems. This review systematically introduces the physiological underpinnings of respiratory and cardiac activities together with their mathematical representations, and elucidates the engineering foundations of radar sensing across different modalities. Furthermore, signal processing pipelines and algorithms are critically examined, covering both traditional frameworks and machine learning–driven approaches, with emphasis on their strengths, limitations, and biomedical performance metrics. Application domains are comprehensively surveyed, spanning bedside clinical monitoring, home healthcare, human–computer interaction, security surveillance, and emerging Internet-of-Things–enabled environments. By integrating current advances, this review highlights the major challenges and outlines prospective research directions, aiming to facilitate the translation of radar-based vital signs sensing into diverse real-world applications.
Original languageEnglish
Pages (from-to)3446-3459
Number of pages14
JournalIEEE Sensors Journal
Volume26
Issue number3
Early online date25 Dec 2025
DOIs
Publication statusPublished - 1 Feb 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Sensors
  • Radar
  • Biomedical monitoring
  • Signal to noise ratio
  • Reviews
  • Measurement
  • Signal processing
  • algorithms
  • Intelligent sensors
  • Heart beat
  • Non-contacted vital signs sensing
  • Machine learning
  • Signal preprocessing and separation
  • Deep learning
  • Human health monitoring

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