Evidence map›Paper›PMID 36292560›Full record

ReviewHealthcare (Basel, Switzerland)2022

Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring.

Ping-Kwan Man, Kit-Leong Cheung, Nawapon Sangsiri, Wilfred Jin Shek, Kwan-Long Wong, Jing-Wei Chin, Tsz-Tai Chan, Richard Hau-Yue So

Open access · goldAbstract readReview
In one paragraph

Review in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
2.5field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 33 citations in OpenAlex.

  1. Trial
  2. Review
  3. Intrinsic PPG-ECG Coupling for Accurate and Low-Power Blood Pressure Monitoring.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
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4 · The record

Corrections and comments

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 at 4 institutions in 2 countries.

Ping-Kwan ManPanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China.
Kit-Leong CheungPanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China.ORCID 0000-0003-2467-1936
Nawapon SangsiriPanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China.
Wilfred Jin ShekPanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China.ORCID 0000-0002-8726-5516
Kwan-Long WongPanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China.ORCID 0000-0002-5847-9734
Jing-Wei ChinPanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China.ORCID 0000-0001-9406-5224
Tsz-Tai ChanPanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China.ORCID 0000-0002-9473-8748
Richard Hau-Yue SoPanopticAI, Hong Kong Science and Technology Parks, New Territories, Hong Kong, China.ORCID 0000-0002-3595-0386
Hong Kong University of Science and Technology · HKChinese University of Hong Kong · HKHong Kong Science and Technology Parks Corporation · HKKing's College London · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Blood pressure (BP) determines whether a person has hypertension and offers implications as to whether he or she could be affected by cardiovascular disease. Cuff-based sphygmomanometers have traditionally provided both accuracy and reliability, but they require bulky equipment and relevant skills to obtain precise measurements. BP measurement from photoplethysmography (PPG) signals has become a promising alternative for convenient and unobtrusive BP monitoring. Moreover, the recent developments in remote photoplethysmography (rPPG) algorithms have enabled new innovations for contactless BP measurement. This paper illustrates the evolution of BP measurement techniques from the biophysical theory, through the development of contact-based BP measurement from PPG signals, and to the modern innovations of contactless BP measurement from rPPG signals. We consolidate knowledge from a diverse background of academic research to highlight the importance of multi-feature analysis for improving measurement accuracy. We conclude with the ongoing challenges, opportunities, and possible future directions in this emerging field of research.

Indexed as

blood pressuredeep learninghemodynamicsmachine learningneural networkphotoplethysmographyremote photoplethysmography

Identifiers

PMID36292560
PMCPMC9601911
OpenAlexW4307271228

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.