FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / Equine Vet J / Agreement between subjective gait assessment and markerless ...
Equine veterinary journal2025; doi: 10.1111/evj.14516

Agreement between subjective gait assessment and markerless video gait-analysis in endurance horses.

Abstract: Subjective evaluation of gait by official endurance veterinarians (OEVs) is used to determine 'fitness-to-compete' in horses participating in endurance competitions. Objective gait analysis systems could aid in quick and verifiable judgements. Objective: To assess the agreement between objective analysis of head and pelvis vertical movement asymmetry performed with a markerless artificial intelligence motion tracking system (AI-MTS) and subjective lameness assessment performed by an accredited FEI OEV to judge horse gaits. Methods: Cross-sectional. Methods: During three endurance competitions, 110 horses were enrolled. The OEV performed 188 gait examinations, which were simultaneously recorded with a smartphone. The vertical motion asymmetry of the head and pelvis was later analysed from the videos through the AI-MTS application. The gaits were scored as 'no asymmetry', 'mild asymmetry' or 'severe asymmetry'. The agreement was evaluated using Fleiss' multi-rater kappa statistic (κ). Results: The overall agreement between the two methods was fair (k = 0.26, p < 0.001). Within the three gait asymmetry categories, substantial agreement was obtained for the 'severe' (k = 0.75, p < 0.001) category, fair agreement was detected for the 'no asymmetry' category (k = 0.25, p < 0.001), and no agreement was identified for the 'mild' category (k = 0.13, p = 0.08). Conclusions: Comparison between AI-MTS and a single OEV; absence of a tripod during video recording; and video recording from a different point of view than the OEVs. Conclusions: Mild asymmetry was the most challenging gait category to identify. Substantial agreement between the subjective lameness evaluation by OEV and AI-MTS assessment was observed for the 'severe' category. AI-MTS may be a helpful tool to assist OEVs in decision-making during endurance competitions.
© 2025 The Author(s). Equine Veterinary Journal published by John Wiley & Sons Ltd on behalf of EVJ Ltd.
Get Full Text
Publication Date: 2025-04-21 PubMed ID: 40257418DOI: 10.1111/evj.14516Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article

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 examines the agreement between the subjective evaluation of a horse’s gait, conducted by official endurance veterinarians (OEVs), and an objective analysis of the horse’s gait using a markerless artificial intelligence motion tracking system (AI-MTS). The study found that the AI-MTS system could potentially be useful in assisting veterinarians during endurance competitions.

Objective of the Research

  • The objective of this research was to investigate the level of agreement between two methods used to evaluate the gait of endurance horses – a subjective assessment by an official endurance veterinarian and an objective evaluation using a markerless artificial intelligence motion tracking system (AI-MTS).

Methodology of the Research

  • The study used a cross-sectional design involving 110 horses from three different endurance competitions.
  • The official endurance veterinarian conducted a total of 188 gait examinations using their subjective evaluation.
  • At the same time, each evaluation was recorded using a smartphone, and the data was later analyzed using the AI-MTS system.
  • The gaits were classified into three levels: no asymmetry, mild asymmetry, and severe asymmetry.
  • The agreement between the two methods was measured using Fleiss’ multi-rater kappa statistic (ĸ).

Results of the Research

  • The research found that the overall agreement between the subjective evaluation by a veterinarian and gait analysis using the AI-MTS system was fair, with a kappa statistic value of 0.26.
  • For the three gait asymmetry categories, the highest level of agreement was found in the ‘severe’ category, with a kappa statistic value of 0.75.
  • The ‘no asymmetry’ category had a fair agreement, with a kappa statistic of 0.25.
  • The agreement for the ‘mild’ category was the lowest and not significant, with a kappa statistic of 0.13.

Conclusions from the Research

  • The results showed that the ‘mild’ category was the most challenging to identify through both methods.
  • There was substantial agreement between the two methods for the ‘severe’ category of gait asymmetry.
  • The research concluded that the AI-MTS can be a helpful tool in making decision-making during endurance competitions by providing a more objective evaluation of a horse’s gait.

Cite This Article

APA
de Chiara M, Montano C, De Matteis A, Guidi L, Buono F, Auletta L, Del Prete C, Pasolini MP. (2025). Agreement between subjective gait assessment and markerless video gait-analysis in endurance horses. Equine Vet J. https://doi.org/10.1111/evj.14516

Publication

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

Researcher Affiliations

de Chiara, Mariaelena
  • Department of Veterinary Medicine and Animal Production, University of Naples Federico II, Naples, Italy.
Montano, Chiara
  • Department of Veterinary Medicine and Animal Production, University of Naples Federico II, Naples, Italy.
De Matteis, Andrea
  • Department of Veterinary Medicine and Animal Production, University of Naples Federico II, Naples, Italy.
Guidi, Livia
  • Freelance Veterinary Practitioner, Avellino, Italy.
Buono, Francesco
  • Department of Veterinary Medicine and Animal Production, University of Naples Federico II, Naples, Italy.
Auletta, Luigi
  • Department of Veterinary Medicine and Animal Science (DIVAS), University of Milan, Lodi, Italy.
Del Prete, Chiara
  • Department of Veterinary Medicine and Animal Production, University of Naples Federico II, Naples, Italy.
Pasolini, Maria Pia
  • Department of Veterinary Medicine and Animal Production, University of Naples Federico II, Naples, Italy.

Grant Funding

  • University of Naples Federico II

References

This article includes 32 references
  1. Fédération Equestre Internationale. Endurance rules. 2024.
  2. Fielding CL, Meier CA, Balch OK, Kass PH. Risk factors for the elimination of endurance horses from competition. J Am Vet Med Assoc 2011;239(4):493–498.
  3. Bloom F, Draper S, Bennet E, Marlin D, Williams J. Risk factors for lameness elimination in British endurance riding. Equine Vet J 2023;55(4):632–641.
  4. Nagy A, Murray JK, Dyson SJ. Horse‐, rider‐, venue‐and environment‐related risk factors for elimination from Fédération Equestre Internationale endurance rides due to lameness and metabolic reasons. Equine Vet J 2014;46(3):294–299.
  5. Nagy A, Murray JK, Dyson SJ. Descriptive epidemiology and risk factors for eliminations from Fédération Equestre Internationale endurance rides due to lameness and metabolic reasons (2008–2011). Equine Vet J 2014;46(1):38–44.
  6. Keegan KG, Dent EV, Wilson DA, Janicek J, Kramer J, Lacarrubba A. Repeatability of subjective evaluation of lameness in horses. Equine Vet J 2010;42(2):92–97.
  7. Keegan KG, Wilson DA, Wilson DJ, Smith B, Gaughan EM, Pleasant RS. Evaluation of mild lameness in horses trotting on a treadmill by clinicians and interns or residents and correlation of their assessments with kinematic gait analysis. Am J Vet Res 1998;59(11):1370–1377.
    doi: 10.2460/ajvr.1998.59.11.1370google scholar: lookup
  8. Starke SD, Oosterlinck M. Reliability of equine visual lameness classification as a function of expertise, lameness severity and rater confidence. Vet Rec 2019;184(2):63.
  9. Pfau T, Landsbergen K, Davis BL, Kenny O, Kernot N, Rochard N. Comparing inertial measurement units to markerless video analysis for movement symmetry in quarter horses. Sensors 2023;23(20):8414.
  10. Martinsson J. Gait screening using a mobile phone application. 2022.
  11. Calle‐González N, Lo Feudo CM, Ferrucci F, Requena F, Stucchi L, Muñoz A. Objective assessment of equine locomotor symmetry using an inertial sensor system and artificial intelligence: a comparative study. Animals 2024;14(6):921.
  12. Lawin FJ, Byström A, Roepstorff C, Rhodin M, Almlöf M, Silva M. Is markerless more or less? Comparing a smartphone computer vision method for equine lameness assessment to multi‐camera motion capture. Animals 2023;13(3):390.
  13. Kallerud AS, Fjordbakk CT, Hendrickson EHS, Persson‐Sjodin E, Hammarberg M, Rhodin M. Objectively measured movement asymmetry in yearling Standardbred trotters. Equine Vet J 2021;53(3):590–599.
  14. Fédération Equestre Internationale. FEI veterinary regulations. 2024.
  15. McHugh ML. The chi‐square test of independence. Biochem Med 2013;23(2):143–149.
    doi: 10.11613/bm.2013.018google scholar: lookup
  16. Hartling L, Hamm M, Milne A, Vandermeer B, Santaguida PL, Ansari M. Validity and inter‐rater reliability testing of quality assessment instruments. 2012.
  17. . PASS 2024 power analysis and sample size software. 2024.
  18. Legg KA, Weston JF, Gee EK, Bolwell CF, Bridges JP, Rogers CW. Characteristics of endurance competitions and risk factors for elimination in New Zealand during six seasons of competition (2010/11–2015/16). Animals 2019;9(9):611.
  19. Paris A, Beccati F, Pepe M. Type, prevalence, and risk factors for the development of orthopedic injuries in endurance horses during training and competition. J Am Vet Med Assoc 2021;258(10):1109–1118.
  20. Bragança FMS, Brommer H, Van Den Belt AJM, Maree JTM, Van Weeren PR, van Oldruitenborgh‐Oosterbaan MMS. Subjective and objective evaluations of horses for fit‐to‐compete or unfit‐to‐compete judgement. Vet J 2020;257:105454.
  21. Zetterberg E, Persson‐Sjodin E, Lundblad J, Hernlund E, Rhodin M. Prevalence of movement asymmetries in high‐performing riding horses perceived as free from lameness and riders' perception of horse sidedness. PLoS One 2024;19(7):e0308061.
  22. van Weeren PR, Pfau T, Rhodin M, Roepstorff L, Serra Bragança F, Weishaupt MA. Do we have to redefine lameness in the era of quantitative gait analysis?. Equine Vet J 2017;49(5):567–569.
  23. Buchner HHF, Savelberg H, Schamhardt HC, Barneveld A. Head and trunk movement adaptations in horses with experimentally induced fore‐or hindlimb lameness. Equine Vet J 1996;28(1):71–76.
  24. Kramer J, Keegan KG, Wilson DA, Smith BK, Wilson DJ. Kinematics of the hind limb in trotting horses after induced lameness of the distal intertarsal and tarsometatarsal joints and intra‐articular administration of anesthetic. Am J Vet Res 2000;61(9):1031–1036.
  25. Bergdal J. Movement patterns in horses with multiple limb lameness. 2024.
  26. Phutthachalee S, Mählmann K, Seesupa S, Lischer C. Upper body movement analysis of multiple limb asymmetry in 367 clinically lame horses. Equine Vet J 2021;53(4):701–709.
  27. De Mira MC, Santos C, Lopes MA, Marlin DJ. Challenges encountered by Federation Equestre Internationale (FEI) veterinarians in gait evaluation during FEI endurance competitions: an international survey. Comp Exerc Physiol 2019;15(5):371–378.
    doi: 10.3920/cep180058google scholar: lookup
  28. Lopes MAF, Eleuterio A, Mira MC. Objective detection and quantification of irregular gait with a portable inertial sensor‐based system in horses during an endurance race—a preliminary assessment. J Equine Vet Sci 2018;70:123–129.
  29. Hardeman AM, Serra Bragança FM, Swagemakers JH, Van Weeren PR, Roepstorff L. Variation in gait parameters used for objective lameness assessment in sound horses at the trot on the straight line and the lunge. Equine Vet J 2019;51(6):831–839.
  30. Briggs EV, Mazzà C. Automatic methods of hoof‐on and‐off detection in horses using wearable inertial sensors during walk and trot on asphalt, sand and grass. PLoS One 2021;16(7):e0254813.
  31. Starke SD, Willems E, May SA, Pfau T. Vertical head and trunk movement adaptations of sound horses trotting in a circle on a hard surface. Vet J 2012;193(1):73–80.
  32. Kallerud AS, Marques‐Smith P, Bendiksen HK, Fjordbakk CT. Objective movement asymmetry in horses is comparable between markerless technology and sensor‐based systems. Equine Vet J 2025;57(1):115–125.
Subscribe to our newsletter
Join 99,344 horse owners like you who receive our email updates on horse health and nutrition.