Quantifying thoracolumbar flexion-extension range of motion in horses at trot

Validation of a handheld smartphone markerless method for quantifying thoracolumbar flexion-extension range of motion in horses at trot

Equine Vet J. 2026 Aug 14. doi: 10.1002/evj.70310. Online ahead of print.
Authors
Maria Rosendahl Christensen 1 , Jakob Kirkegaard 2 , Katja Berg 2 , Kristian Ringkjær Andresen 1 , Sabrina Skov Hansen 3 , Karsten Key 2
Affiliations

1 iKeyVet, Fredensborg, Denmark.
2 Keydiagnostics (RealHorse®), Fredensborg, Denmark.
3 Department of Veterinary Clinical Sciences, University of Copenhagen, Copenhagen, Denmark.

PMID: 42598797
DOI: 10.1002/evj.70310

Abstract

Background: Back pain and poor performance in horses are difficult to assess objectively and thoracolumbar flexion-extension range of motion (FEROM) is not reliably evaluated by subjective observation alone. A handheld, smartphone-based markerless computer vision system (RealHorse®; RH) may enable objective clinical and field-based FEROM assessment but requires validation against an established optical motion capture reference system (Qualisys®; QS).

Objectives: To compare the accuracy and precision of RH relative to QS for measuring FEROM in horses trotting on a straight line and on a circle.

Study design: Cross-sectional comparative validation study.

Methods: Fifty-nine horses were recorded trotting on a straight line; 23 of these were also recorded on a circle. Data were collected simultaneously using RH and QS, with a light source for manual frame-level synchronisation. The validation videos of horses were not used to train the RH algorithm. Agreement between RH and QS was analysed separately for straight-line and circular trot at stride and trial levels using mean signed error (MSE), mean absolute error (MAE), and Bland-Altman limits of agreement (LoA).

Results: On the straight-line, stride-level MSE was -0.08°, MAE 0.96°, and LoA -2.49° to 2.32°. Trial-level agreement was high, with MSE -0.13°, MAE 0.44°, and LoA -1.22° to 0.94°. On the circle, stride-level MSE was -0.71°, MAE 1.14°, and LoA -3.17° to 1.74°. At the trial-level, variability was moderate (MSE -0.62°, MAE 0.78°, LoA -2.18° to 0.93°). Overall agreement was lower on the circle than on the straight-line and higher at trial-level than at stride-level.

Conclusions: Clinically, RH may support objective clinic and field-based monitoring of thoracolumbar flexion-extension range of motion, especially for repeated within-horse assessments, but decision thresholds remain to be established. Stride-level agreement between RH and QS was influenced by expected stride-to-stride variability, whereas averaging across strides improved agreement at the trial-level.

Keywords: back kinematics; equine gait analysis; flexion extension; horse; markerless computer vision; thoracolumbar motion.

© 2026 The Author(s). Equine Veterinary Journal published by John Wiley & Sons Ltd on behalf of EVJ Ltd.

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