Evidence map›Paper›PMID 38434043›Full record

ArticleHeliyon2024

Robust blood pressure measurement from facial videos in diverse environments.

Jin-Soo Park, Kwang-Seok Hong

Abstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Review
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

2 authors.

Jin-Soo ParkDepartment of Electrical and Computer Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si, 16419, Republic of Korea.
Kwang-Seok HongSchool of Electronic Electrical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon-si, 16419, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Blood pressure (BP) management is important worldwide, and BP monitoring is a crucial aspect of maintaining good health. Traditional BP meter measures BP independently in various situations, such as at home or work, using a cuff to maintain a stable condition. However, these devices can causes a foreign body sensation and discomfort, and are not always practical for periodic monitoring. As a result, studies have been conducted on the use of photoplethysmography (PPG) for measuring BP. However, PPG also has limitations similar to those of traditional BP meters, as it requires the placement of sensors on two regions of the body (fingers or toes). To address this issue, researchers have conducted studies on non-contact methods for measuring BP using face and hand videos. These studies have utilized two cameras to measure PTT and have focused on internal environments, resulting in low accuracy of BP measurement in external environments. We proposes a method for robust BP measurement using pulse wave velocity (PWV) and PTT calculated from facial videos. PTT is estimated by measuring the phase difference between two different regions of interest (ROIs) and PWV is calculated using PTT and the actual distance between two ROIs. In addition, our proposed method extracts the pulse wave from the ROI to measure BP. The actual distance between the ROIs and PTT are estimated using the two extracted pulse waves, and BP is then measured using PWV and PTT. To evaluate the BP measurement performance, the BP calculated from both BP meters and facial videos (in indoor, outdoor, driving car, and flying drone environments) are compared. Our results reveal that the proposed method can robustly measure BP in diverse environments.

Indexed as

Facial videoNoncontactNoninvasivePTTPWVRobust blood pressure

Identifiers

PMID38434043
PMCPMC10906170

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.