Evidence map›Paper›PMID 37430667›Full record

ArticleSensors (Basel, Switzerland)2023

Non-Contact Monitoring of Fetal Movement Using Abdominal Video Recording.

Qiao Han, Dongmei Hao, Lin Yang, Yimin Yang, Guangfei Li

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. 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
1.2field-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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Monitoring SFSensors (Basel, Switzerland) · 2024
    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

5 authors at 1 institution in 1 country.

Qiao HanFaculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
Dongmei HaoFaculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.ORCID 0000-0002-5715-5930
Lin YangFaculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
Yimin YangFaculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
Guangfei LiFaculty of Environment and Life, Beijing University of Technology, Beijing 100124, China.
Beijing University of Technology · CN

Funding

National Natural Science Foundation of China U20A20388the National Key R&D Program 2019YFC0119700
6 · The paper itself

Abstract

Fetal movement (FM) is an important indicator of fetal health. However, the current methods of FM detection are unsuitable for ambulatory or long-term observation. This paper proposes a non-contact method for monitoring FM. We recorded abdominal videos from pregnant women and then detected the maternal abdominal region within each frame. FM signals were acquired by optical flow color-coding, ensemble empirical mode decomposition, energy ratio, and correlation analysis. FM spikes, indicating the occurrence of FMs, were recognized using the differential threshold method. FM parameters including number, interval, duration, and percentage were calculated, and good agreement was found with the manual labeling performed by the professionals, achieving true detection rate, positive predictive value, sensitivity, accuracy, and F1_score of 95.75%, 95.26%, 95.75%, 91.40%, and 95.50%, respectively. The changes in FM parameters with gestational week were consistent with pregnancy progress. In general, this study provides a novel contactless FM monitoring technology for use at home.

Indexed as

AbdomenFetal MovementFemaleFetal MonitoringHumansPregnancyVideo RecordingVideotape Recordingfetal movementnon-contactoptical flowvideo

Identifiers

PMID37430667
PMCPMC10222361
OpenAlexW4382790669

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.