Continuous Blood Pressure Estimation in Wearable Devices Using Photoplethysmography: A Review

Angie Medina, Nikolai Lopez, Jarelh Galdos, Elvis Supo, Jorge Rendulich, Erasmo Sulla

Research output: Contribution to journalArticlepeer-review


Cardiovascular diseases (CVD) are among those with the highest mortality rates, and various wearable devices for continuous monitoring are emerging as a complement to medical procedures. Blood pressure (BP) monitoring in wearable devices, in order to be continuous, must be performed noninvasively, thus involving photoplethysmography (PPG), a technology that has been widely studied in recent years as a non-invasive solution for BP estimation. However, continuous data acquisition in a wearable system is still a challenge, one of the reasons being the noise caused by movement, the correct use of the PPG signal, and the estimation method to be used. This paper reviews the advances in blood pressure estimation based on photoplethysmography, focusing on the analysis of the preprocessing (ICA, FIR, adaptive filters) of the signals. Among the filters reviewed, the most suitable for dealing with Motion Artifacts (MA) of a wearable system are the adaptive filters, because conventional filters are limited to work only in the band for which they are designed, which does not always cover the spectrum of the MA. A review of the estimation methods is also carried out, among them machine learning stands out because it shows greater growth due to the new proposals that use more signals and obtain better results in terms of accuracy. The objective is to know and analyze the appropriate preprocessing filters and estimation methods from the perspective of wearable systems using PPG sensors affected by AM.

Original languageEnglish
Pages (from-to)104-113
Number of pages10
JournalInternational Journal of Emerging Technology and Advanced Engineering
Issue number10
StatePublished - 1 Oct 2022

Bibliographical note

Funding Information:
This work is part of a research project funded by the Universidad Nacional de San Agustın de Arequipa, contract number IBA-IB-63-2020-UNSA, April 2021 - March 2023.

Publisher Copyright:
© 2022 Authors. All rights reserved.


  • adaptive filtering
  • Blood Pressure Estimation
  • Machine Learning
  • PAT
  • Photoplethysmography
  • PTT


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