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Evaluating the Accuracy of a Vision-Based Algorithm for Groundline Estimation in Trotting Horses Using Multiple

Karsten Key1, Katja Berg1, Jakob Kirkegaard1

  • 1KeyDiagnostics, Fredensborg, Denmark.

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|December 30, 2025
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Summary

This study shows a new vision-based algorithm accurately estimates the groundline for equine lameness diagnosis using various camera angles, including handheld devices. This offers a portable and objective alternative to traditional methods.

Keywords:
deep learningequine lamenessgroundline estimationhandheldlameness detectionobjective gait analysispose estimationvision‐based algorithm

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Area of Science:

  • Veterinary Medicine
  • Biomechanics
  • Computer Vision

Background:

  • Equine lameness diagnosis often relies on subjective visual assessments.
  • Objective methods like IMUs require specialized equipment.
  • Vision-based algorithms present a portable, markerless alternative needing validation.

Purpose of the Study:

  • To evaluate a custom vision-based algorithm for groundline estimation.
  • To assess accuracy across multiple camera angles, including handheld use.
  • To analyze its application in horses trotting on a treadmill.

Main Methods:

  • An experimental comparative study involving eight Standardbred mares.
  • Used iPhones for video recording at various angles, including handheld.
  • Applied a deep neural network to estimate 2D keypoints and compute Vertical Displacement Signals (VDS).

Main Results:

  • Groundline estimation showed near-zero mean angle error and low mean average error (MAE = 0.45°).
  • Stride-level MAE for Maxdiff and Mindiff was 0.5 mm.
  • Handheld use introduced clinically acceptable additional variability (Maxdiff and Mindiff MAE < 1.8 mm).

Conclusions:

  • The vision-based algorithm accurately estimates groundline and VDS parameters from diverse camera setups.
  • Limitations include treadmill-based data and single breed/coat color, potentially affecting generalizability.
  • Further validation in varied environments and against other objective systems is recommended.