An Overview of Algorithms for Contactless Cardiac Feature Extraction from Radar Signals: Advances and Challenges
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Abstract (arXiv)
Contactless cardiac monitoring has vast potential to replace contact-based monitoring in various future scenarios such as smart home and in-cabin monitoring. Various contactless sensors can be potentially implemented for cardiac monitoring, such as cameras, acoustic sensors, Wi-Fi routers and radars. Among all these sensors, radar could achieve unobtrusive monitoring with high accuracy and robustness at the same time. The research about radar-based cardiac monitoring can be generally divided into the radar architecture design and signal-processing parts, where the former has been thoroughly reviewed in the literature but not the latter. To the best of the author knowledge, this is the first review paper that focuses on elaborating the algorithms for extracting cardiac features from the received radar signal. In addition, a new taxonomy is proposed to reveal the core feature of each algorithm, with the pros and cons evaluated in detail. Furthermore, the public datasets containing the received radar signal and ground-truth cardiac feature signal are listed with detailed configurations, and the corresponding evaluations may help the researchers select the suitable dataset. At last, several unsolved challenges and future directions are suggested and discussed in detail to encourage future research on solving the main obstacles in this field. In summary, this review can be served as a guide for researchers and practitioners to quickly understand the research trend and recent development of the cardiac feature extraction algorithms, and it is worth further investigating the relative area based on the proposed challenges and future directions.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 4 Aug 2025.
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Citing papers per year: 2025: 1; 2026: 3.
4 papers.
- 2026Bayesian Signal Component Decomposition via Diffusion-within-Gibbs Samplingcited by 1
- 2026Physics-Constrained Deep Learning Model for Contactless Blood Pressure Monitoring from Triaxial Bodyseismographycited by 1
- 2025LifWavNet: Lifting Wavelet-based Network for Non-contact ECG Reconstruction from Radarcited by 0
- 2026Kapture: Capturing Cardiac Dynamics with Koopman-Governed Learning for Efficient Radar-Based Electrocardiogram Recoverycited by 0
- 2026Kapture: Capturing Cardiac Dynamics with Koopman-Governed Learning for Efficient Radar-Based Electrocardiogram Recovery4 Oct 2026
- 2026Physics-Constrained Deep Learning Model for Contactless Blood Pressure Monitoring from Triaxial Bodyseismography24 Aug 2026
- 2026Bayesian Signal Component Decomposition via Diffusion-within-Gibbs Sampling11 Feb 2026
- 2025LifWavNet: Lifting Wavelet-based Network for Non-contact ECG Reconstruction from Radar31 Oct 2025