Evidence map›Paper›PMID 41546456›Full record

ArticleEquine veterinary journal2026

Validation of a handheld smartphone markerless gait-analysis tool using an estimated groundline in horses.

Karsten Key, Jakob Kirkegaard, Katja Berg, Kristian Ringkjær Andresen, Sabrina Skov Hansen

Abstract readValidation Study
In one paragraph

Article in Equine veterinary journal, 2026. 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

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2 · The registry

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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

5 authors.

Karsten KeyKeydiagnostics (RealHorse®), Fredensborg, Denmark.ORCID https://orcid.org/0009-0004-4678-2316
Jakob KirkegaardKeydiagnostics (RealHorse®), Fredensborg, Denmark.
Katja BergKeydiagnostics (RealHorse®), Fredensborg, Denmark.
Kristian Ringkjær AndreseniKeyVet®, Fredensborg, Denmark.
Sabrina Skov HansenDepartment of Veterinary Clinical Sciences, University of Copenhagen, Copenhagen, Denmark.ORCID https://orcid.org/0000-0003-4227-9890

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA handheld smartphone-based computer vision algorithm (RealHorse® [RH]) offers accessible alternatives for equine gait analysis but requires validation against a gold-standard three-dimensional multicamera optical motion capture system (Qualisys® [QS]).

objectivesTo evaluate the accuracy and precision of RH in measuring vertical displacement signals (VDS) at the eye, withers, back and croup in horses trotting on a straight line and on a circle. STUDY

designCross-sectional comparative validation study of a markerless computer vision algorithm.

methodsFifty-nine horses were recorded while trotting on a straight line and 24 were lunged on a circle. RH detected two-dimensional anatomical keypoints on each frame, which were used to estimate a dynamic groundline and compute ground relative VDS with stride-based difference in maxima (Maxdiff) and minima (Mindiff). QS provided synchronous ground-relative VDS reference values. Agreement was evaluated using mean signed error, mean absolute error and Bland-Altman analysis.

resultsOn the straight line (n = 2620 strides), the pooled stride-level MAE for Maxdiff and Mindiff was 3.8 mm. Keypoint-specific errors were 5.1 mm (eye), 4.3 mm (withers) and 3.0 mm (croup). On the circle (n = 2419 strides), pooled stride-level error increased to 5.5 mm. Trial-level analysis (n = 58 trials) showed much lower errors: 1.4 mm for both eye and withers and 1.1 mm for croup. On the circle (n = 24 trials), trial-level errors were higher, with 2.8 mm for the eye, 1.8 mm for the withers and 3.3 mm for the croup. The back keypoint consistently showed the lowest errors across both stride and trial levels. MAIN LIMITATIONS: RH measurements of the croup Mindiff during circling resulted in higher values and showed the largest error.

conclusionsRH measured vertical displacement of all keypoints with high accuracy and precision (trial-level MAE 1.1-1.4 mm straight, 1.8-3.3 mm circle), supporting its use for equine gait analysis.

Indexed as

GaitGait AnalysisSmartphoneAlgorithmsAnimalsCross-Sectional StudiesFemaleHorsesMotion Capturecomputer vision algorithmequine lamenessgait analysisGroundlinehorseMaxdiffMindiffvision‐based algorithm

Identifiers

PMID41546456
PMCPMC13447902

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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.