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  • Review Article
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Wearable blood pressure sensors for cardiovascular monitoring and machine learning algorithms for blood pressure estimation

Nature Reviews Cardiology volume 22, pages 629–648 (2025)Cite this article

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A Publisher Correction to this article was published on 04 July 2025

This article has been updated

Abstract

With advances in materials science and medical technology, wearable sensors have become crucial tools for the early diagnosis and continuous monitoring of numerous cardiovascular diseases, including arrhythmias, hypertension and coronary artery disease. These devices employ various sensing mechanisms, such as mechanoelectric, optoelectronic, ultrasonic and electrophysiological methods, to measure vital biosignals, including pulse rate, blood pressure and changes in heart rhythm. In this Review, we provide a comprehensive overview of the current state of wearable cardiovascular sensors, focusing particularly on those that measure blood pressure. We explore biosignal sensing principles, discuss blood pressure estimation methods (including machine learning algorithms) and summarize the latest advances in cuffless wearable blood pressure sensors. Finally, we highlight the challenges of and offer insights into potential pathways for the practical application of cuffless wearable blood pressure sensors in the medical field from both technical and clinical perspectives.

Key points

  • Wearable blood pressure (BP) sensors utilize diverse sensing methodologies, including mechanoelectric, optoelectronic, ultrasonic and electrophysiologic technologies, that facilitate continuous cardiovascular monitoring.

  • Various approaches, including pulse wave analysis, pulse wave velocity and arterial wall dynamics, as well as advanced machine learning and deep learning algorithms that build on these methods, are being explored to improve the accuracy of BP estimation in wearable cuffless BP sensors.

  • Cuffless BP sensors still face obstacles in achieving clinical-grade reliability due to issues with sensor calibration, motion artefacts and placement accuracy.

  • Further improvements in sensor materials and system integration are crucial for improving the accuracy and clinical applicability of wearable BP sensors.

  • Comprehensive clinical trials are essential to validate the performance of wearable BP sensors and ensure compliance with established medical standards for broader adoption in health-care settings.

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Fig. 1: Wearable BP sensors for cardiovascular health care.
Fig. 2: Principles of biosignal data acquisition related to BP.
Fig. 3: Biosignal analysis theories for BP estimation.
Fig. 4: Deep learning algorithms for advanced BP estimation.
Fig. 5: Advances in wearable BP sensors.

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Acknowledgements

The authors receive support from the National Research Foundation of Korea (NRF) by grants funded by the Korean government (MSIT; RS-2024-00406240 and RS-2023-00273231).

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Author notes

  1. These authors contributed equally: Seongwook Min, Jaehun An, Jae Hee Lee.

Authors and Affiliations

  1. Department of Materials Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea

    Seongwook Min, Jaehun An, Ji Hoon Kim & Keon Jae Lee

  2. Querrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL, USA

    Jae Hee Lee & John A. Rogers

  3. Safety Measurement Institute, Korea Research Institute of Standards and Science (KRISS), Daejeon, Republic of Korea

    Daniel J. Joe

  4. Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea

    Soo Hwan Eom & Chang D. Yoo

  5. Department of Internal Medicine, Division of Cardiology, Uijeongbu St Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

    Hyo-Suk Ahn

  6. Department of Anaesthesiology and Pain Medicine, SMG-SNU Boramae Medical Center, College of Medicine, Seoul National University, Seoul, Republic of Korea

    Jin-Young Hwang

  7. Department of Nanoengineering, University of California San Diego, La Jolla, CA, USA

    Sheng Xu

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Contributions

S.M., J.A., J.H.L. and J.H.K. researched data for the article; S.M., J.A., J.H.L. and S.H.E. wrote the manuscript; S.M., J.A., J.H.L., H.-S.A. and J.-Y.H. contributed to the discussion of its content; and K.J.L., D.J.J., C.D.Y., S.X. and J.A.R. reviewed or edited the manuscript before submission.

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Correspondence to Keon Jae Lee.

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Min, S., An, J., Lee, J.H. et al. Wearable blood pressure sensors for cardiovascular monitoring and machine learning algorithms for blood pressure estimation. Nat Rev Cardiol 22, 629–648 (2025). https://doi.org/10.1038/s41569-025-01127-0

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