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

  • Seongwook Min
  • , Jaehun An
  • , Jae Hee Lee
  • , Ji Hoon Kim
  • , Daniel J. Joe
  • , Soo Hwan Eom
  • , Chang D. Yoo
  • , Hyo Suk Ahn
  • , Jin Young Hwang
  • , Sheng Xu
  • , John A. Rogers
  • , Keon Jae Lee
  • Korea Advanced Institute of Science and Technology
  • Northwestern University
  • Korea Research Institute of Standards and Science
  • Seoul National University
  • University of California at San Diego

Research output: Contribution to journalReview articlepeer-review

113 Scopus citations

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.

Original languageEnglish
Pages (from-to)629-648
Number of pages20
JournalNature Reviews Cardiology
Volume22
Issue number9
DOIs
StatePublished - Sep 2025

Bibliographical note

Publisher Copyright:
© Springer Nature Limited 2025.

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

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