Validity of a Convolutional Neural Network-Based, Markerless Pose Estimation System Compared to a Marker-Based 3D Motion Analysis System for Gait Assessment-A Pilot Study.

Korbinian Ksoll, Rafael Krätschmer, Fabian Stöcker

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Korbinian KsollOPED GmbH, 83626 Valley/Oberlaindern, Germany.ORCID 0000-0003-2864-0969

Rafael KrätschmerPrevention Center, Department Health and Sport Sciences, TUM School of Medicine and Health, Technical University of Munich, 80809 Munich, Germany.

Fabian StöckerPrevention Center, Department Health and Sport Sciences, TUM School of Medicine and Health, Technical University of Munich, 80809 Munich, Germany.

Funding

OPED GmbH, Valley/Oberlaindern, German n.a.

6 · The paper itself

Abstract

Gait analysis is a valuable tool for a wide range of clinical applications. Until now, the standard for gait analysis has been marker-based 3D optical systems. Recently, markerless gait analysis systems that utilize pose estimation models based on Convolutional Neural Networks (CNNs) and computer vision have gained importance. In this pilot study, we validated the performance of a CNN-based, markerless pose estimation algorithm (Orthelligent

Indexed as

GaitGait AnalysisImaging, Three-DimensionalNeural Networks, ComputerPostureAdultAlgorithmsConvolutional Neural NetworksFemaleHumansMaleMotionPilot ProjectsYoung Adult3D motion capturecoefficient of variation (CV)concordance correlation coefficient (CCC)convolutional neural network (CNN)gait analysisgait variabilitymarkerless pose estimationorthelligent vision

Identifiers

PMID41228774

DOI10.3390/s25216551

PMCPMC12609618

What Socratic 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 Socratic graph.