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Equine veterinary journal2026; doi: 10.1002/evj.70149

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

Abstract: A 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]). Objective: To 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. Methods: Cross-sectional comparative validation study of a markerless computer vision algorithm. Methods: Fifty-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. Results: On 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. Conclusions: RH measurements of the croup Mindiff during circling resulted in higher values and showed the largest error. Conclusions: RH 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.
© 2026 The Author(s). Equine Veterinary Journal published by John Wiley & Sons Ltd on behalf of EVJ Ltd.
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Publication Date: 2026-01-16 PubMed ID: 41546456DOI: 10.1002/evj.70149Google Scholar: Lookup
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Summary

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Overview

  • This study evaluated the accuracy and precision of a handheld smartphone-based gait analysis tool (RealHorse®) by comparing it to a high-quality, established motion capture system (Qualisys®) when measuring vertical movements of horse body parts during trotting.

Introduction

  • The RealHorse® (RH) tool uses smartphone cameras and computer vision to track horse gait without markers, making it potentially accessible and practical for routine use.
  • Validating RH against the gold-standard Qualisys® (QS) 3D motion capture system is necessary to confirm if RH reliably measures vertical displacements in key anatomical points during horse locomotion.

Objective

  • To assess how accurately and precisely RH measures vertical displacement signals (VDS) at four anatomical points: eye, withers, back, and croup in horses trotting in two conditions — straight-line and circular paths.

Methods

  • Participants:
    • Fifty-nine horses recorded trotting on a straight line.
    • Twenty-four horses lunged on a circular track.
  • Data Collection:
    • RH recorded video using smartphone cameras and detected 2D anatomical keypoints frame-by-frame.
    • The keypoints were used to estimate a dynamic groundline, allowing calculation of vertical displacement relative to the ground.
    • Displacement metrics were stride-based differences in maxima (Maxdiff) and minima (Mindiff).
    • QS simultaneously captured 3D ground-relative VDS reference data to compare with RH results.
  • Data Analysis:
    • Errors between RH and QS measurements were calculated using mean signed error (bias), mean absolute error (MAE), and Bland-Altman agreement analysis.
    • Analysis was performed both at the stride-level (individual steps) and trial-level (entire run) for more generalized accuracy assessment.

Results

  • Straight-Line Trotting:
    • Total of 2,620 strides analyzed.
    • Pooled stride-level MAE was 3.8 mm for Maxdiff and Mindiff combined.
    • Errors by keypoint:
      • Eye: 5.1 mm
      • Withers: 4.3 mm
      • Croup: 3.0 mm
    • At the trial level (58 trials), errors were smaller:
      • Eye and withers: 1.4 mm
      • Croup: 1.1 mm
  • Circular Trotting (lunging):
    • 2,419 strides analyzed.
    • Stride-level pooled MAE increased to 5.5 mm.
    • Trial-level error (24 trials) was higher than straight line:
      • Eye: 2.8 mm
      • Withers: 1.8 mm
      • Croup: 3.3 mm
    • The back keypoint consistently showed the lowest error across all analyses.
    • The croup Mindiff during circling had the largest errors and higher displacement values, indicating this was the most challenging measurement for RH.

Conclusions

  • The smartphone-based RH tool demonstrated high accuracy and precision in measuring vertical displacements of key anatomical points of trotting horses compared to the 3D motion capture gold standard.
  • Errors at the trial level were generally very low (1.1 to 3.3 mm), indicating reliable measurements sufficient for clinical or research gait analysis applications.
  • Greater errors during circular trotting, especially at the croup, suggest some limitations in measurement under certain conditions but still within an acceptable range.
  • Overall, RH presents a feasible, accessible option for markerless equine gait analysis outside of specialized lab environments.

Cite This Article

APA
Key K, Kirkegaard J, Berg K, Andresen KR, Skov Hansen S. (2026). Validation of a handheld smartphone markerless gait-analysis tool using an estimated groundline in horses. Equine Vet J. https://doi.org/10.1002/evj.70149

Publication

Equine veterinary journal
ISSN: 2042-3306
NlmUniqueID: 0173320
Country: United States
Language: English

Researcher Affiliations

Key, Karsten
  • Keydiagnostics (RealHorse®), Fredensborg, Denmark.
Kirkegaard, Jakob
  • Keydiagnostics (RealHorse®), Fredensborg, Denmark.
Berg, Katja
  • Keydiagnostics (RealHorse®), Fredensborg, Denmark.
Andresen, Kristian Ringkjær
  • iKeyVet®, Fredensborg, Denmark.
Skov Hansen, Sabrina
  • Department of Veterinary Clinical Sciences, University of Copenhagen, Copenhagen, Denmark.

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