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Home / Equine Research Bank / Equine Vet J / Objective movement asymmetry in horses is comparable between...
Equine veterinary journal2024; doi: 10.1111/evj.14089

Objective movement asymmetry in horses is comparable between markerless technology and sensor-based systems.

Abstract: A markerless artificial intelligence (AI) system for lameness detection has recently become available but has not been extensively compared with commonly used inertial measurement unit (IMU) systems for detecting asymmetry under field conditions. Objective: Comparison of classification of asymmetric limbs under field conditions and comparison of normalised asymmetry data using a markerless AI system (SleipAI; recorded on a tripod mounted iPhone 14pro [SL]); the Equinosis Q Lameness Locator (LL); the EquiMoves (EM); and subjective evaluation (SE). Methods: Descriptive clinical study. Methods: Straight line trot data were collected from 52 client-owned horses in regular training. Limbs were categorised as symmetric or asymmetric. Number of analysed strides were compared with Wilcoxon's each pairs test. Inter-rater reliability in classification of asymmetric limbs was assessed with Light's Kappa. Bland Altman analysis of normalised asymmetry data was performed. Results: Data from 41 horses were included. Most horses showed mild asymmetry. The EM analysed significantly more strides than the other systems, both for forelimbs and for hindlimbs (53 ± 11 strides for both, respectively; p < 0.006). The LL analysed significantly more hindlimbs strides (45 ± 13) than the SL (27 ± 6; p < 0.001). Moderate inter-rater agreement for asymmetry classification was found between systems (k = 0.59 forelimbs; 0.44 hindlimbs); agreement decreased when including the SE. For the normalised asymmetry data, the strongest agreement was found between the two IMU systems. Conclusions: Horses were assessed during straight-line trot only. Conclusions: The objective systems were comparable in classification of asymmetric limbs under field conditions when using defined asymmetry thresholds. Discrepancies stemmed largely from the imposed thresholds (i.e., systems largely identified same-side asymmetry). Overall, the strongest agreement was found between LL and EM. The SL analysed significantly fewer hindlimb strides than the LL and EM which could represent a limitation of the Sleip AI.
© 2024 The Authors. Equine Veterinary Journal published by John Wiley & Sons Ltd on behalf of EVJ Ltd.
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Publication Date: 2024-04-02 PubMed ID: 38566453DOI: 10.1111/evj.14089Google Scholar: Lookup
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Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This research article focuses on the comparison of a markerless artificial intelligence system for lameness detection in horses with common inertial measurement unit (IMU) systems under real-world conditions. The main finding supports the hypothesis that both markerless technology and sensor-based approaches are equivalently effective in categorizing limb asymmetry when using established thresholds.

Methods of the Study

  • The researchers carried out a descriptive clinical study where data were gathered from 52 client-owned horses.
  • Utilizing a markerless AI system (SleipAI), the Equinosis Q Lameness Locator (LL), and the EquiMoves (EM), they captured the horses’ straight line trot details.
  • Different limbs were classified as symmetric or asymmetric, and the various systems analyzed the number of strides.
  • The researchers used Wilcoxon’s each pairs test to compare analyzed strides numbers and Light’s Kappa to determine the inter-rater reliability for limb asymmetry classification.
  • Finally, they performed a Bland Altman analysis on the normalized asymmetry data.

Findings of the Study

  • The results utilized 41 horses’ data, with most of these horses demonstrating a slight asymmetry in their movement.
  • The EquiMoves system was found to analyze significantly more strides than the other systems for both fore and hindlimbs.
  • On the other hand, the Lameness Locator system analyzed more hindlimb strides than the markerless SleipAI system.
  • There was a moderate agreement between systems when classifying limb asymmetry, but the agreement lessened when subjective evaluation was included.
  • In terms of analyzing normalized asymmetry data, the highest agreement was found between the two IMU systems (LL and EM).

Conclusions

  • The study limited its assessments of the horses during straight-line trotting only.
  • The systems are suitably comparative in terms of classifying asymmetric limbs under field conditions when designated asymmetry thresholds were utilized.
  • The main differences essentially arose from the defined thresholds, in that they identified the asymmetry on the same side.
  • Moreover, the strongest agreement was found between the Lameness Locator and EquiMoves systems.
  • One notable limitation of the SleipAI system observed was it analyzed considerably fewer hindlimb strides than the LL and EM systems.

Cite This Article

APA
Kallerud AS, Marques-Smith P, Bendiksen HK, Fjordbakk CT. (2024). Objective movement asymmetry in horses is comparable between markerless technology and sensor-based systems. Equine Vet J. https://doi.org/10.1111/evj.14089

Publication

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

Researcher Affiliations

Kallerud, Anne S
  • Department of Companion Animal Clinical Sciences, Norwegian University of Life Sciences, Aas, Norway.
Marques-Smith, Patrick
  • Department of Companion Animal Clinical Sciences, Norwegian University of Life Sciences, Aas, Norway.
Bendiksen, Helle K
  • Veterinærene Bendiksen & Smith, Aas, Norway.
Fjordbakk, Cathrine T
  • Department of Companion Animal Clinical Sciences, Norwegian University of Life Sciences, Aas, Norway.

Grant Funding

  • Norwegian University of Life Sciences (NMBU)

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Citations

This article has been cited 6 times.
  1. Key K, Berg K, Kirkegaard J, Andresen KR, Hansen SS. Evaluating the Accuracy of a Vision-Based Algorithm for Groundline Estimation in Trotting Horses Using Multiple Camera Angles. Vet Med Sci 2026 Jan;12(1):e70739.
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  2. Shaffer SK, Medjaouri O, Swenson B, Eliason T, Nicolella DP. A Markerless Approach for Full-Body Biomechanics of Horses. Animals (Basel) 2025 Aug 5;15(15).
    doi: 10.3390/ani15152281pubmed: 40805071google scholar: lookup
  3. Geiger T, Lindenhahn L, Delarocque J, Geburek F. Evaluation of water treadmill training, lunging and treadmill training in the rehabilitation of horses with back pain. BMC Vet Res 2025 Jul 29;21(1):495.
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  4. Meistro F, Ralletti MV, Rinnovati R, Spadari A. Objective Evaluation of Gait Asymmetries in Traditional Racehorses During Pre-Race Inspection: Application of a Markerless AI System in Straight-Line and Lungeing Conditions. Animals (Basel) 2025 Jun 18;15(12).
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  5. de Chiara M, Montano C, De Matteis A, Guidi L, Buono F, Auletta L, Del Prete C, Pasolini MP. Agreement between subjective gait assessment and markerless video gait-analysis in endurance horses. Equine Vet J 2026 Jan;58(1):60-67.
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