Evidence map›Paper›PMID 41114895›Full record

ArticlePhysical and engineering sciences in medicine2025

A non-contact blood pressure measurement method based on face video.

Lifeng Yang, Shaojie Gu, Binbin Liu, Junjie Wang, Junwei Cheng, Yuanxi Zhang, Zhengan Xia, Yan Yang

Abstract read
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Article in Physical and engineering sciences in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Lifeng YangSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
Shaojie GuSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
Binbin LiuSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
Junjie WangSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
Junwei ChengSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
Yuanxi ZhangSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
Zhengan XiaSchool of Optoelectronic Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
Yan YangDepartment of Endocrinology and Metabolism, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, 610072, People's Republic of China. yangyannf@med.uestc.edu.cn.

Funding

Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China ZYGX2021YGLH206
6 · The paper itself

Abstract

Blood pressure is an essential indicator of cardiovascular health in the human body, and regular and accurate blood pressure measurement is essential for preventing cardiovascular diseases. The emergence of photoplethysmography (PPG) and the advancement of machine learning offers new opportunities for noninvasive blood pressure measurement. This paper proposes a non-contact method for measuring blood pressure using face video and machine learning. This method extracts facial remote photoplethysmography (RPPG) signals from face video captured by a camera, and enhances the signal quality of RPPG through a set of filtering processes. The blood pressure regression model is constructed using the extreme gradient boosting tree (XGBoost) method to estimate blood pressure from RPPG signals. This approach achieved accurate blood pressure measurement, with a measurement error of 4.8893 ± 6.6237 mmHg for systolic pressure and 4.0805 ± 5.5821 mmHg for diastolic pressure. Experimental results show that this method fully complies with the American Medical Instrumentation Association (AAMI).Our proposed method has minor errors in predicting the systolic and diastolic blood pressures and achieves grade A evaluation for both systolic and diastolic blood pressures according to the British Hypertension Society (BHS) standards.

Indexed as

Blood Pressure DeterminationFaceVideo RecordingAdultBlood PressureFemaleHumansMachine LearningMalePhotoplethysmographySignal Processing, Computer-AssistedBlood pressureMachine learningPulse wave analysisRemote photoplethysmography

Identifiers

PMID41114895

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.