Evidence map›Paper›PMID 37420630›Full record

ArticleSensors (Basel, Switzerland)2023

Visual Gait Analysis Based on UE4.

Ruzhang Liu, Luyin Liu, Guochao Ma, Shanshan Feng, Yuanhui Mu, Dexi Meng, Shuying Wang, Enlin Cai

Abstract 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
–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. 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

8 authors.

Ruzhang LiuThe School of Electronic Information, Qingdao University, Qingdao 266071, China.ORCID 0000-0003-4527-8607
Luyin LiuThe School of Electronic Information, Qingdao University, Qingdao 266071, China.
Guochao MaThe School of Electronic Information, Qingdao University, Qingdao 266071, China.
Shanshan FengThe School of Electronic Information, Qingdao University, Qingdao 266071, China.
Yuanhui MuThe School of Electronic Information, Qingdao University, Qingdao 266071, China.
Dexi MengThe School of Electronic Information, Qingdao University, Qingdao 266071, China.
Shuying WangThe School of Electronic Information, Qingdao University, Qingdao 266071, China.
Enlin CaiThe School of Electronic Information, Qingdao University, Qingdao 266071, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the development of artificial intelligence technology, virtual reality technology has been widely used in the medical and entertainment fields, as well as other fields. This study is supported by the 3D modeling platform in UE4 platform technology and designs a 3D pose model based on inertial sensors through blueprint language and C++ programming. It can vividly display changes in gait, as well as changes in angles and displacements of 12 parts such as the big and small legs and arms. It can be used to combine with the module of capturing motion which is based on inertial sensors to display the 3D posture of the human body in real-time and analyze the motion data. Each part of the model contains an independent coordinate system, which can analyze the angle and displacement changes of any part of the model. All joints of the model are interrelated, the motion data can be automatically calibrated and corrected, and errors measured by an inertial sensor can be compensated, so that each joint of the model will not separate from the whole model and there will not occur actions that against the human body's structures, improving the accuracy of the data. The 3D pose model designed in this study can correct motion data in real time and display the human body's motion posture, which has great application prospects in the field of gait analysis.

Indexed as

Artificial IntelligenceGait AnalysisBiomechanical PhenomenaGaitHumansMotionPosture3D gait analysisUE4 enginevirtual realityvisualization

Identifiers

PMID37420630
PMCPMC10303037

What OpenQuestion holds

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Registered trials

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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.