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Home / Equine Research Bank / Sensors (Basel) / Discrimination of the Lame Limb in Horses Using a Machine Le...
Sensors (Basel, Switzerland)2025; 25(4); 1095; doi: 10.3390/s25041095

Discrimination of the Lame Limb in Horses Using a Machine Learning Method (Support Vector Machine) Based on Asymmetry Indices Measured by the EQUISYM System.

Abstract: Lameness detection in horses is a critical challenge in equine veterinary practice, particularly when symptoms are mild. This study aimed to develop a predictive system using a support vector machine (SVM) to identify the affected limb in horses trotting in a straight line. The system analyzed data from inertial measurement units (IMUs) placed on the horse's head, withers, and pelvis, using variables such as vertical displacement and retraction angles. A total of 287 horses were included, with 256 showing single-limb lameness and 31 classified as sound. The model achieved an overall accuracy of 86%, with the highest success rates in identifying right and left forelimb lameness. However, there were challenges in identifying sound horses, with a 54.8% accuracy rate, and misclassification between forelimb and hindlimb lameness occurred in some cases. The study highlighted the importance of specific variables, such as vertical head and withers displacement, for accurate classification. Future research should focus on refining the model, exploring deep learning methods, and reducing the number of sensors required, with the goal of integrating these systems into equestrian equipment for early detection of locomotor issues.
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Publication Date: 2025-02-12 PubMed ID: 40006323PubMed Central: PMC11858852DOI: 10.3390/s25041095Google 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.

The study presents a machine learning technique (Support Vector Machine) aimed at distinguishing the lame limb in horses by analyzing asymmetry indices obtained from an EQUISYM system.

Introduction

In the field of equine veterinary practice, detecting lameness in horses can pose a significant challenge, especially when the symptoms are slight or mild. Understanding which limb is affected is crucial for diagnosis and treatment. This research endeavored to devise a predictive model utilizing an SVM (Support Vector Machine) to accurately pinpoint the injured limb in trotting horses.

Methodology

  • The researchers used data from IMUs (Inertial Measurement Units) that were strategically placed on the horse’s head, withers, and pelvis.
  • The SVM processed several variables, including vertical displacement and retraction angles.
  • A set of 287 horses were involved in this study, 256 of them were exhibiting single-limb lameness, and the remaining 31 were categorized as sound or healthy.

Results and Discussion

  • The developed machine learning model showed an overall accuracy of 86% in identifying the lame limb. It was particularly successful in detecting lameness in the right and left forelimbs.
  • However, the model had limitations in accurately identifying sound or healthy horses; it had an accuracy rate of only 54.8% in this aspect.
  • In addition, some cases of misclassification between forelimb and hindlimb lameness were reported.
  • The research underscored the importance of certain variables, such as vertical displacement of the head and withers, in ensuring precise classification of the lame limb.

Conclusion and Future Directions

  • The study proved to be a promising step forward in developing effective systems for detecting lameness in horses.
  • Suggestions for future research include refining the SVM model, exploring the use of deep learning techniques, and lessening the number of sensors needed for monitoring.
  • The end goal is to be able to incorporate these systems into equestrian equipment to promptly detect locomotor issues and save horses from unnecessary suffering.

Cite This Article

APA
Poizat E, Gérard M, Macaire C, De Azevedo E, Denoix JM, Coudry V, Jacquet S, Bertoni L, Tallaj A, Audigié F, Hatrisse C, Hébert C, Martin P, Marin F, Hanne-Poujade S, Chateau H. (2025). Discrimination of the Lame Limb in Horses Using a Machine Learning Method (Support Vector Machine) Based on Asymmetry Indices Measured by the EQUISYM System. Sensors (Basel), 25(4), 1095. https://doi.org/10.3390/s25041095

Publication

Sensors (Basel, Switzerland)
ISSN: 1424-8220
NlmUniqueID: 101204366
Country: Switzerland
Language: English
Volume: 25
Issue: 4
PII: 1095

Researcher Affiliations

Poizat, Emma
  • Centre d'Imagerie et de Recherche sur les Affections Locomotrices Equines (CIRALE), Ecole Nationale vétérinaire d'Alfort, 94700 Maisons-Alfort, France.
Gérard, Mahaut
  • Centre d'Imagerie et de Recherche sur les Affections Locomotrices Equines (CIRALE), Ecole Nationale vétérinaire d'Alfort, 94700 Maisons-Alfort, France.
  • LIM France, Labcom LIM-EnvA, 24300 Nontron, France.
Macaire, Claire
  • Centre d'Imagerie et de Recherche sur les Affections Locomotrices Equines (CIRALE), Ecole Nationale vétérinaire d'Alfort, 94700 Maisons-Alfort, France.
  • LIM France, Labcom LIM-EnvA, 24300 Nontron, France.
  • Laboratoire de BioMécanique et BioIngénierie (UMR CNRS 7338), Centre of Excellence for Human and Animal Movement Biomechanics (CoEMoB), Université de Technologie de Compiègne (UTC), Alliance Sorbonne Université, 60200 Compiègne, France.
De Azevedo, Emeline
  • Centre d'Imagerie et de Recherche sur les Affections Locomotrices Equines (CIRALE), Ecole Nationale vétérinaire d'Alfort, 94700 Maisons-Alfort, France.
Denoix, Jean-Marie
  • Centre d'Imagerie et de Recherche sur les Affections Locomotrices Equines (CIRALE), Ecole Nationale vétérinaire d'Alfort, 94700 Maisons-Alfort, France.
Coudry, Virginie
  • Centre d'Imagerie et de Recherche sur les Affections Locomotrices Equines (CIRALE), Ecole Nationale vétérinaire d'Alfort, 94700 Maisons-Alfort, France.
Jacquet, Sandrine
  • Centre d'Imagerie et de Recherche sur les Affections Locomotrices Equines (CIRALE), Ecole Nationale vétérinaire d'Alfort, 94700 Maisons-Alfort, France.
Bertoni, Lélia
  • Centre d'Imagerie et de Recherche sur les Affections Locomotrices Equines (CIRALE), Ecole Nationale vétérinaire d'Alfort, 94700 Maisons-Alfort, France.
Tallaj, Amélie
  • Centre d'Imagerie et de Recherche sur les Affections Locomotrices Equines (CIRALE), Ecole Nationale vétérinaire d'Alfort, 94700 Maisons-Alfort, France.
Audigié, Fabrice
  • Centre d'Imagerie et de Recherche sur les Affections Locomotrices Equines (CIRALE), Ecole Nationale vétérinaire d'Alfort, 94700 Maisons-Alfort, France.
Hatrisse, Chloé
  • Centre d'Imagerie et de Recherche sur les Affections Locomotrices Equines (CIRALE), Ecole Nationale vétérinaire d'Alfort, 94700 Maisons-Alfort, France.
Hébert, Camille
  • LIM France, Labcom LIM-EnvA, 24300 Nontron, France.
Martin, Pauline
  • LIM France, Labcom LIM-EnvA, 24300 Nontron, France.
Marin, Frédéric
  • Laboratoire de BioMécanique et BioIngénierie (UMR CNRS 7338), Centre of Excellence for Human and Animal Movement Biomechanics (CoEMoB), Université de Technologie de Compiègne (UTC), Alliance Sorbonne Université, 60200 Compiègne, France.
Hanne-Poujade, Sandrine
  • LIM France, Labcom LIM-EnvA, 24300 Nontron, France.
Chateau, Henry
  • Centre d'Imagerie et de Recherche sur les Affections Locomotrices Equines (CIRALE), Ecole Nationale vétérinaire d'Alfort, 94700 Maisons-Alfort, France.

MeSH Terms

  • Animals
  • Horses
  • Support Vector Machine
  • Lameness, Animal / diagnosis
  • Lameness, Animal / physiopathology
  • Forelimb / physiology
  • Horse Diseases / diagnosis
  • Machine Learning
  • Hindlimb / physiology
  • Gait / physiology

Grant Funding

  • ANR 16-LCV2-0002-01 / Agence Nationale de la Recherche
  • 20E01636 / Ru00e9gion Normandie
  • 20E01636 / European Regional Development Fund

Conflict of Interest Statement

The project was supported by the Agence Nationale de la Recherche (ANR), which sponsors a collaborative project between a company (LIM France) and the Ecole Nationale Vétérinaire d’Alfort (ENVA).

References

This article includes 62 references
  1. Loomans J.B.A., Stolk P.W.T., van Weeren P.R., Vaarkamp H., Barneveld A.. A Survey of the Workload and Clinical Skills in Current Equine Practices in The Netherlands. Equine Vet. Educ. 2007;19:162–168.
    doi: 10.2746/095777307X186875google scholar: lookup
  2. Keegan K.G., Dent E.V., Wilson D.A., Janicek J., Kramer J., Lacarrubba A., Walsh D.M., Cassells M.W., Esther T.M., Schiltz P.. Repeatability of Subjective Evaluation of Lameness in Horses. Equine Vet. J. 2010;42:92–97.
    doi: 10.2746/042516409X479568pubmed: 20156242google scholar: lookup
  3. McCracken M.J., Kramer J., Keegan K.G., Lopes M., Wilson D.A., Reed S.K., LaCarrubba A., Rasch M.. Comparison of an Inertial Sensor System of Lameness Quantification with Subjective Lameness Evaluation. Equine Vet. J. 2012;44:652–656.
    doi: 10.1111/j.2042-3306.2012.00571.xpubmed: 22563674google scholar: lookup
  4. Parkes R.S.V., Weller R., Groth A.M., May S., Pfau T.. Evidence of the Development of “domain-Restricted” Expertise in the Recognition of Asymmetric Motion Characteristics of Hindlimb Lameness in the Horse. Equine Vet. J. 2009;41:112–117.
    doi: 10.2746/042516408X343000pubmed: 19418737google scholar: lookup
  5. Keegan K.G., Wilson D.A., Wilson D.J., Smith B., Gaughan E.M., Pleasant R.S., Lillich J.D., Kramer J., Howard R.D., Bacon-Miller C.. 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;59:1370–1377.
    doi: 10.2460/ajvr.1998.59.11.1370pubmed: 9829392google scholar: lookup
  6. Timmerman I., Macaire C., Hanne-Poujade S., Bertoni L., Martin P., Marin F., Chateau H.. A Pilot Study on the Inter-Operator Reproducibility of a Wireless Sensors-Based System for Quantifying Gait Asymmetries in Horses. Sensors 2022;22:9533.
    doi: 10.3390/s22239533pmc: PMC9740227pubmed: 36502233google scholar: lookup
  7. Cruz A.M., Maninchedda U.E., Burger D., Wanda S., Vidondo B.. Repeatability of Gait Pattern Variables Measured by Use of Extremity-Mounted Inertial Measurement Units in Nonlame Horses during Trotting. Am. J. Vet. Res. 2017;78:1011–1018.
    doi: 10.2460/ajvr.78.9.1011pubmed: 28836845google scholar: lookup
  8. Audigié F., Pourcelot P., Degueurce C., Geiger D., Denoix J.M.. Fourier Analysis of Trunk Displacements: A Method to Identify the Lame Limb in Trotting Horses. J. Biomech. 2002;35:1173–1182.
    doi: 10.1016/S0021-9290(02)00089-1pubmed: 12163307google scholar: lookup
  9. Scheidegger M.D., Gerber V., Dolf G., Burger D., Flammer A., Ramseyer A.. Quantitative Gait Analysis before and after a Cross-Country Test in a Population of Elite Eventing Horses. J. Equine Vet. Sci. 2022;117:104077.
    doi: 10.1016/j.jevs.2022.104077pubmed: 35820497google scholar: lookup
  10. Rhodin M., Persson-Sjodin E., Egenvall A., Serra Bragança F.M., Pfau T., Roepstorff L., Weishaupt M.A., Thomsen M.H., van Weeren P.R., Hernlund E.. Vertical Movement Symmetry of the Withers in Horses with Induced Forelimb and Hindlimb Lameness at Trot. Equine Vet. J. 2018;50:818–824.
    doi: 10.1111/evj.12844pmc: PMC6175082pubmed: 29658147google scholar: lookup
  11. Marunova E., Hoenecke K., Fiske-Jackson A., Smith R.K.W., Bolt D.M., Perrier M., Gerdes C., Hernlund E., Rhodin M., Pfau T.. Changes in Head, Withers, and Pelvis Movement Asymmetry in Lame Horses as a Function of Diagnostic Anesthesia Outcome, Surface and Direction. J. Equine Vet. Sci. 2022;118:104136.
    doi: 10.1016/j.jevs.2022.104136pubmed: 36210019google scholar: lookup
  12. Vorstenbosch M.A., Buchner H.H., Savelberg H.H., Schamhardt H.C., Barneveld A.. Modeling Study of Compensatory Head Movements in Lame Horses. Am. J. Vet. Res. 1997;58:713–718.
    doi: 10.2460/ajvr.1997.58.07.713pubmed: 9215445google scholar: lookup
  13. Persson-Sjodin E., Hernlund E., Pfau T., Andersen P.H., Forsström K.H., Byström A., Serra Bragança F.M., Hardeman A., Greve L., Egenvall A.. Withers Vertical Movement Symmetry Is Useful for Locating the Primary Lame Limb in Naturally Occurring Lameness. Equine Vet. J. 2024;56:76–88.
    doi: 10.1111/evj.13947pubmed: 37226583google scholar: lookup
  14. Peham C., Scheidl M., Licka T.. A Method of Signal Processing in Motion Analysis of the Trotting Horse. J. Biomech. 1996;29:1111–1114.
    doi: 10.1016/0021-9290(95)00179-4pubmed: 8817379google scholar: lookup
  15. Pfau T., Noordwijk K., Sepulveda Caviedes M.F., Persson-Sjodin E., Barstow A., Forbes B., Rhodin M.. Head, Withers and Pelvic Movement Asymmetry and Their Relative Timing in Trot in Racing Thoroughbreds in Training. Equine Vet. J. 2018;50:117–124.
    doi: 10.1111/evj.12705pmc: PMC5724686pubmed: 28548349google scholar: lookup
  16. Macaire C., Hanne-Poujade S., De Azevedo E., Denoix J.-M., Coudry V., Jacquet S., Bertoni L., Tallaj A., Audigié F., Hatrisse C.. Investigation of Thresholds for Asymmetry Indices to Represent the Visual Assessment of Single Limb Lameness by Expert Veterinarians on Horses Trotting in a Straight Line. Animals 2022;12:3498.
    doi: 10.3390/ani12243498pmc: PMC9774792pubmed: 36552418google scholar: lookup
  17. Schobesberger H., Peham C.. Computerized Detection of Supporting Forelimb Lameness in the Horse Using an Artificial Neural Network. Vet. J. 2002;163:77–84.
    doi: 10.1053/tvjl.2001.0608pubmed: 11749140google scholar: lookup
  18. Wu X., Ma L., Wei P., Shan Y., Chan P., Wang K., Zhao G.. Wearable Sensor Devices Can Automatically Identify the ON-OFF Status of Patients with Parkinson’s Disease through an Interpretable Machine Learning Model. Front. Neurol. 2024;15:1387477.
    doi: 10.3389/fneur.2024.1387477pmc: PMC11094303pubmed: 38751881google scholar: lookup
  19. Borzì L., Mazzetta I., Zampogna A., Suppa A., Olmo G., Irrera F.. Prediction of Freezing of Gait in Parkinson’s Disease Using Wearables and Machine Learning. Sensors 2021;21:614.
    doi: 10.3390/s21020614pmc: PMC7830634pubmed: 33477323google scholar: lookup
  20. Marimon X., Mengual I., López-de-Celis C., Portela A., Rodríguez-Sanz J., Herráez I.A., Pérez-Bellmunt A.. Kinematic Analysis of Human Gait in Healthy Young Adults Using IMU Sensors: Exploring Relevant Machine Learning Features for Clinical Applications. Bioengineering 2024;11:105.
    doi: 10.3390/bioengineering11020105pmc: PMC10886386pubmed: 38391591google scholar: lookup
  21. Frohlich H., Claes K., De Wolf C., Van Damme X., Michel A.. A Machine Learning Approach to Automated Gait Analysis for the Noldus Catwalk System. IEEE Trans. Biomed. Eng. 2018;65:1133–1139.
    doi: 10.1109/TBME.2017.2701204pubmed: 28858780google scholar: lookup
  22. Tufféry S.. Data Mining and Statistics for Decision Making. John Wiley & Sons; Hoboken, NJ, USA: 2011.
  23. Bonaccorso G.. Machine Learning Algorithms: Popular Algorithms for Data Science and Machine Learning. 2nd ed. Packt Publishing Ltd.; Birmingham, UK: 2018.
  24. Mahesh B.. Machine Learning Algorithms—A Review. Int. J. Sci. Res. 2018;9:381–386.
    doi: 10.21275/ART20203995google scholar: lookup
  25. Hassan F., Moawed S., El-Araby I., Gouda H.. Machine Learning Based Prediction for Solving Veterinary Data Problems: A Review. J. Adv. Vet. Res. 2022;12:798–802.
  26. Basran P.S., Appleby R.B.. The Unmet Potential of Artificial Intelligence in Veterinary Medicine. ajvr 2022;83:385–392.
    doi: 10.2460/ajvr.22.03.0038pubmed: 35353711google scholar: lookup
  27. Pfau T., Robilliard J.J., Weller R., Jespers K., Eliashar E., Wilson A.M.. Assessment of Mild Hindlimb Lameness during over Ground Locomotion Using Linear Discriminant Analysis of Inertial Sensor Data. Equine Vet. J. 2007;39:407–413.
    doi: 10.2746/042516407X185719pubmed: 17910264google scholar: lookup
  28. Ben-Hur A., Weston J.. A User’s Guide to Support Vector Machines. Methods Mol. Biol. 2010;609:223–239.
    doi: 10.1007/978-1-60327-241-4_13pubmed: 20221922google scholar: lookup
  29. Chen B., Chen C., Hu J., Sayeed Z., Qi J., Darwiche H.F., Little B.E., Lou S., Darwish M., Foote C.. Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction. Sensors 2022;22:7960.
    doi: 10.3390/s22207960pmc: PMC9612353pubmed: 36298311google scholar: lookup
  30. Serra Bragança F.M., Broomé S., Rhodin M., Björnsdóttir S., Gunnarsson V., Voskamp J.P., Persson-Sjodin E., Back W., Lindgren G., Novoa-Bravo M.. Improving Gait Classification in Horses by Using Inertial Measurement Unit (IMU) Generated Data and Machine Learning. Sci. Rep. 2020;10:17785.
    doi: 10.1038/s41598-020-73215-9pmc: PMC7576586pubmed: 33082367google scholar: lookup
  31. Darbandi H., Serra Bragança F., van der Zwaag B.J., Voskamp J., Gmel A.I., Haraldsdóttir E.H., Havinga P.. Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses—A Machine Learning Approach. Sensors 2021;21:798.
    doi: 10.3390/s21030798pmc: PMC7865839pubmed: 33530288google scholar: lookup
  32. Parmentier J.I.M., Bragança F.M.S., Hernlund E., van der Zwaag B.J.. Terrain Type Detection for Smart Equine Gait Analysis Systems Using Inertial Sensors and Machine Learning. Proceedings of the 2023 19th International Conference on Distributed Computing in Smart Systems and the Internet of Things (DCOSS-IoT); Pafos, Cyprus. 19–21 June 2023; pp. 103–111.
  33. Gokcen I., Peng J.. Comparing Linear Discriminant Analysis and Support Vector Machines. Advances in Information Systems. Volume 2457. Springer; Berlin/Heidelberg, Germany: Oct 23, 2002; pp. 104–113.
  34. Yu H., Deng J., Nathan R., Kröschel M., Pekarsky S., Li G., Klaassen M.. An Evaluation of Machine Learning Classifiers for Next-Generation, Continuous-Ethogram Smart Trackers. Mov. Ecol. 2021;9:15.
    doi: 10.1186/s40462-021-00245-xpmc: PMC8011142pubmed: 33785056google scholar: lookup
  35. Uddin S., Khan A., Hossain M.E., Moni M.A.. Comparing Different Supervised Machine Learning Algorithms for Disease Prediction. BMC Med. Inform. Decis. Mak. 2019;19:281.
    doi: 10.1186/s12911-019-1004-8pmc: PMC6925840pubmed: 31864346google scholar: lookup
  36. Keegan K.G., Arafat S., Skubic M., Wilson D.A., Kramer J.. Detection of Lameness and Determination of the Affected Forelimb in Horses by Use of Continuous Wavelet Transformation and Neural Network Classification of Kinematic Data. Am. J. Vet. Res. 2003;64:1376–1381.
    doi: 10.2460/ajvr.2003.64.1376pubmed: 14620773google scholar: lookup
  37. May S.A., Wyn-Jones G.. Identification of Hindleg Lameness. Equine Vet. J. 1987;19:185–188.
    doi: 10.1111/j.2042-3306.1987.tb01371.xpubmed: 3608952google scholar: lookup
  38. Dyson S.. Can Lameness Be Graded Reliably?: Can Lameness Be Graded Reliably?. Equine Vet. J. 2011;43:379–382.
    doi: 10.1111/j.2042-3306.2011.00391.xpubmed: 21631579google scholar: lookup
  39. Arkell M., Archer R.M., Guitian F.J., May S.A.. Evidence of Bias Affecting the Interpretation of the Results of Local Anaesthetic Nerve Blocks When Assessing Lameness in Horses. Vet. Rec. 2006;159:346–348.
    doi: 10.1136/vr.159.11.346pubmed: 16963714google scholar: lookup
  40. Macaire C., Hanne-Poujade S., De Azevedo E., Denoix J.-M., Coudry V., Jacquet S., Bertoni L., Tallaj A., Audigié F., Hatrisse C.. Asymmetry Thresholds Reflecting the Visual Assessment of Forelimb Lameness on Circles on a Hard Surface. Animals 2023;13:3319.
    doi: 10.3390/ani13213319pmc: PMC10650068pubmed: 37958073google scholar: lookup
  41. Efron B., Tibshirani R.J.. An Introduction to the Bootstrap. CRC Press; Boca Raton, FL, USA: 1994.
  42. Kuhn M.. Building Predictive Models in R Using the Caret Package. J. Stat. Softw. 2008;28:1–26.
    doi: 10.18637/jss.v028.i05pubmed: 0google scholar: lookup
  43. Means K., Hayden L., Kramer J., McCracken M.J., Reed S.K., Wilson D.A., Keegan K.G.. Vertical Pelvic Movement Asymmetry and Lameness Location in Ipsilateral Combined Forelimb and Hindlimb Lameness Cases. Equine Vet. J. 2024;57:362–374.
    doi: 10.1111/evj.14117pubmed: 38923053google scholar: lookup
  44. Keegan K.G., Pai P.F., Wilson D.A., Smith B.K.. Signal Decomposition Method of Evaluating Head Movement to Measure Induced Forelimb Lameness in Horses Trotting on a Treadmill. Equine Vet. J. 2010;33:446–451.
    doi: 10.2746/042516401776254781pubmed: 11558738google scholar: lookup
  45. Kallerud A.S., Fjordbakk C.T., Hendrickson E.H.S., Persson-Sjodin E., Hammarberg M., Rhodin M., Hernlund E.. Objectively Measured Movement Asymmetry in Yearling Standardbred Trotters. Equine Vet. J. 2021;53:590–599.
    doi: 10.1111/evj.13302pubmed: 32558997google scholar: lookup
  46. Hardeman A.M., Serra Bragança F.M., Swagemakers J.H., van Weeren P.R., 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;51:831–839.
    doi: 10.1111/evj.13075pmc: PMC6850282pubmed: 30648286google scholar: lookup
  47. Weeren P.R., Pfau T., Rhodin M., Roepstorff L., Serra Bragança F., Weishaupt M.A.. Do We Have to Redefine Lameness in the Era of Quantitative Gait Analysis?. Equine Vet. J. 2017;49:567–569.
    doi: 10.1111/evj.12715pubmed: 28804943google scholar: lookup
  48. 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:e0308061.
    doi: 10.1371/journal.pone.0308061pmc: PMC11288442pubmed: 39078818google scholar: lookup
  49. Pfau T.. Sensor-Based Equine Gait Analysis: More than Meets the Eye?. UK-Vet. Equine. 2019;3:102–112.
    doi: 10.12968/ukve.2019.3.3.102google scholar: lookup
  50. Ishihara I., Al B., Pj R.-S.. Association between Subjective Lameness Grade and Kinetic Gait Parameters in Horses with Experimentally Induced Forelimb Lameness. Am. J. Vet. Res. 2005;66:1805–1815.
    doi: 10.2460/ajvr.2005.66.1805pubmed: 16273915google scholar: lookup
  51. Peham C., Licka T., Girtler D., Scheidl M.. Supporting Forelimb Lameness: Clinical Judgement vs. Computerised Symmetry Measurement. Equine Vet. J. 1999;31:417–421.
    doi: 10.1111/j.2042-3306.1999.tb03842.xpubmed: 10505958google scholar: lookup
  52. Barrey E., Hermelin M., Vaudelin J.L., Poirel D., Valette J.P.. Utilisation of an Accelerometric Device in Equine Gait Analysis. Equine Vet. J. 1994;26:7–12.
    doi: 10.1111/j.2042-3306.1994.tb04864.xgoogle scholar: lookup
  53. Peham C., Licka T., Girtler D., Scheidl M.. The Influence of Lameness on Equine Stride Length Consistency. Vet. J. 2001;162:153–157.
    doi: 10.1053/tvjl.2001.0593pubmed: 11531399google scholar: lookup
  54. Barrey E., Desbrosse F.. Lameness Detection Using an Accelerometric Device. Pferdeheilkunde 1996;12:617–622.
    doi: 10.21836/PEM19960456google scholar: lookup
  55. Barrey E., AUVINET B., Couroucé A.. Gait Evaluation of Race Trotters Using an Accelerometric Device. Equine Vet. J. 2010;27:156–160.
    doi: 10.1111/j.2042-3306.1995.tb04910.xgoogle scholar: lookup
  56. Serra Bragança F.M., Hernlund E., Thomsen M.H., Waldern N.M., Rhodin M., Byström A., Weeren P.R., Weishaupt M.A.. Adaptation Strategies of Horses with Induced Forelimb Lameness Walking on a Treadmill. Equine Vet. J. 2021;53:600–611.
    doi: 10.1111/evj.13344pmc: PMC8048804pubmed: 32888199google scholar: lookup
  57. Buchner H.H.F., Savelberg H.H.C.M., Schamhardt H.C., Barneveld A.. Head and Trunk Movement Adaptations in Horses with Experimentally Induced Fore- or Hindlimb Lameness. Equine Vet. J. 1996;28:71–76.
    doi: 10.1111/j.2042-3306.1996.tb01592.xpubmed: 8565958google scholar: lookup
  58. Pfau T., Bolt D.M., Fiske-Jackson A., Gerdes C., Hoenecke K., Lynch L., Perrier M., Smith R.K.W.. Linear Discriminant Analysis for Investigating Differences in Upper Body Movement Symmetry in Horses before/after Diagnostic Analgesia in Relation to Expert Judgement. Animals 2022;12:762.
    doi: 10.3390/ani12060762pmc: PMC8944550pubmed: 35327159google scholar: lookup
  59. . Movement Symmetry of the Withers Can Be Used to Discriminate Primary Forelimb Lameness From Compensatory Forelimb Asymmetry in Horses with Induced Lameness. Equine Vet. J. 2016;48:32–33.
    doi: 10.1111/evj.64_12595google scholar: lookup
  60. Marunova E., Dod L., Witte S., Pfau T.. Smartphone-Based Pelvic Movement Asymmetry Measures for Clinical Decision Making in Equine Lameness Assessment. Animals 2021;11:1665.
    doi: 10.3390/ani11061665pmc: PMC8228485pubmed: 34204921google scholar: lookup
  61. Parmentier J.I.M., Bosch S., van der Zwaag B.J., Weishaupt M.A., Gmel A.I., Havinga P.J.M., van Weeren P.R., Braganca F.M.S.. Prediction of Continuous and Discrete Kinetic Parameters in Horses from Inertial Measurement Units Data Using Recurrent Artificial Neural Networks. Sci. Rep. 2023;13:740.
    doi: 10.1038/s41598-023-27899-4pmc: PMC9839734pubmed: 36639409google scholar: lookup
  62. Yigit T., Han F., Rankins E., Yi J., McKeever K., Malinowski K.. Wearable Inertial Sensor-Based Limb Lameness Detection and Pose Estimation for Horses. IEEE Trans. Autom. Sci. Eng. 2022;19:1365–1379.
    doi: 10.1109/TASE.2022.3157793google scholar: lookup

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