Evidence map›Paper›PMID 34101126›Full record

ArticleMedical & biological engineering & computing2021

A real-time camera-based adaptive breathing monitoring system.

Yu-Ching Lee, Abdan Syakura, Muhammad Adil Khalil, Ching-Ho Wu, Yi-Fang Ding, Ching-Wei Wang

Open access · bronzeAbstract read
In one paragraph

Article in Medical & biological engineering & computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
1.5field-weighted citation impact, top 19% 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 22 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
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

6 authors at 3 institutions in 1 country.

Yu-Ching LeeGraduate institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei City, Taiwan.
Abdan SyakuraGraduate institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei City, Taiwan.
Muhammad Adil KhalilGraduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei City, Taiwan.
Ching-Ho WuInstitute of Veterinary Clinical Sciences, National Taiwan University, Taipei City, Taiwan.
Yi-Fang DingDepartment of Otolaryngology, Wan Fang Hospital, Taipei Medical University, Taipei City, Taiwan. 102008@w.tmu.edu.tw.
Ching-Wei WangGraduate institute of Applied Science and Technology, National Taiwan University of Science and Technology, Taipei City, Taiwan. cweiwang@mail.ntust.edu.tw.ORCID http://orcid.org/0000-0001-9992-6863
National Taiwan University of Science and Technology · TWNational Taiwan University · TWTaipei Medical University Hospital · TW

Funding

Ministry of Science and Technology, Taiwan MOST108-2221-E-011-070Ministry of Science and Technology, Taiwan MOST109-2221-E-011-018-MY3Taipei Wan Fang Hospital 107-wf-swf-03Tri-Service General Hospital-National Taiwan University of Science and Technology TSGH-NTUST-103-02
6 · The paper itself

Abstract

Breathing is one of the vital signs used to assess the physical health of a subject. Non-contact-based measurements of both breathing rate and changes in breathing rate help monitor health condition of subjects more flexibly. In this paper, we present an improved real-time camera-based adaptive breathing monitoring system, which includes real time (1) adaptive breathing motion detection, (2) adaptive region of interest detection to eliminate environmental noise, (3) breathing and body movement classification, (4) respiration rate estimation, (5) monitor change in respiration rate to examine overall health of an individual, and (6) online adaptation to lighting. The proposed system does not pose any positional and postural constraint. For evaluation, 30 videos of 15 animals are tested with drugs to simulate various medical conditions and breathing patterns, and the results from the proposed system are compared with the outputs of an existing FDA-approved invasive medical system for patient monitoring. The results show that the proposed method performs significantly correlated RR results to the reference medical device with the correlation coefficient equal to 0.92 and p-value less than 0.001, and more importantly the proposed video-based method is demonstrated to produce alarms 10 to 20 s earlier than the benchmark medical device. Graphical abstract The proposed system flowchart to extract the respiratory pattern from video.

Indexed as

RespirationRespiratory RateAlgorithmsHumansMonitoring, PhysiologicMotionMovementNon-contact-based breathing monitoringRespiration rate measurementVision-based respiratory rate

Identifiers

PMID34101126
PMCPMC8185321
OpenAlexW3168814267

What OpenQuestion holds

Textmetadata
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.