FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / Animals (Basel) / Horse Jumping and Dressage Training Activity Detection Using...
Animals : an open access journal from MDPI2021; 11(10); doi: 10.3390/ani11102904

Horse Jumping and Dressage Training Activity Detection Using Accelerometer Data.

Abstract: Equine training activity detection will help to track and enhance the performance and fitness level of riders and their horses. Currently, the equestrian world is eager for a simple solution that goes beyond detecting basic gaits, yet current technologies fall short on the level of user friendliness and detection of main horse training activities. To this end, we collected leg accelerometer data of 14 well-trained horses during jumping and dressage trainings. For the first time, 6 jumping training and 25 advanced horse dressage activities are classified using specifically developed models based on a neural network. A jumping training could be classified with a high accuracy of 100 %, while a dressage training could be classified with an accuracy of 96.29%. Assigning the dressage movements to 11, 6 or 4 superclasses results in higher accuracies of 98.87%, 99.10% and 100%, respectively. Furthermore, during dressage training, the side of movement could be identified with an accuracy of 97.08%. In addition, a velocity estimation model was developed based on the measured velocities of seven horses performing the collected, working, and extended gaits during a dressage training. For the walk, trot, and canter paces, the velocities could be estimated accurately with a low root mean square error of 0.07 m/s, 0.14 m/s, and 0.42 m/s, respectively.
Get Full Text
Publication Date: 2021-10-07 PubMed ID: 34679925PubMed Central: PMC8532712DOI: 10.3390/ani11102904Google 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.

The research investigates the effectiveness of using accelerometer data to detect and classify equine training activities such as horse jumping and dressage. The data collected showed a high accuracy rate, providing a potential solution for the equestrian world to track and enhance rider and horse performance.

Collecting and Using Accelerometer Data

  • The study collected leg accelerometer data from 14 well-trained horses during jumping and dressage trainings.
  • Based on this data, they developed specific models, using a neural network, to classify the activities being conducted.
  • The study highlights the ability of this methodology to distinguish different horse training activities beyond basic gaits, something the current technology struggles with.

Accuracy of Activity Classification

  • Jumping training activities were accurately classified 100% of the time, signifying the reliability of the models developed.
  • The dressage training activities were accurately classified 96.29% of the time.
  • Accuracy improved when dressage activities were grouped into larger superclass categories, with accuracies of 98.87%, 99.10%, and 100% when sorted into 11, 6, or 4 superclasses respectively.
  • The horse’s side of movement during dressage training was identified with 97.08% accuracy.
  • Overall these high percentages suggest a great potential for using this data in improving performance understanding and tracking.

Velocity Estimation Model

  • Alongside activity classification, the researchers developed a velocity estimation model.
  • This model was based on the measured velocities of seven horses performing various gaits during a dressage training.
  • For walk, trot, and canter paces, the velocities could be accurately measured with a low root mean square error.
  • The estimated velocities had errors of only 0.07 m/s, 0.14 m/s, and 0.42 m/s respectively, further adding to the potential application of these methods in trainings.

Cite This Article

APA
Eerdekens A, Deruyck M, Fontaine J, Damiaans B, Martens L, De Poorter E, Govaere J, Plets D, Joseph W. (2021). Horse Jumping and Dressage Training Activity Detection Using Accelerometer Data. Animals (Basel), 11(10). https://doi.org/10.3390/ani11102904

Publication

Animals : an open access journal from MDPI
ISSN: 2076-2615
NlmUniqueID: 101635614
Country: Switzerland
Language: English
Volume: 11
Issue: 10

Researcher Affiliations

Eerdekens, Anniek
  • WAVES-IMEC, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium.
Deruyck, Margot
  • WAVES-IMEC, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium.
Fontaine, Jaron
  • IDLab-IMEC, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium.
Damiaans, Bert
  • VETMED, Department of Reproduction, Obstetrics and Herd Health, Faculty of Veterinary Medicine, Ghent University, 9820 Merelbeke, Belgium.
Martens, Luc
  • WAVES-IMEC, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium.
De Poorter, Eli
  • IDLab-IMEC, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium.
Govaere, Jan
  • VETMED, Department of Reproduction, Obstetrics and Herd Health, Faculty of Veterinary Medicine, Ghent University, 9820 Merelbeke, Belgium.
Plets, David
  • WAVES-IMEC, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium.
Joseph, Wout
  • WAVES-IMEC, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium.

Conflict of Interest Statement

The authors declare no conflict of interest.

References

This article includes 39 references
  1. Visser E.K., Van Wijk-Jansen E.E.. Diversity in horse enthusiasts with respect to horse welfare: An explorative study.. J. Vet. Behav. 2012;7:295–304.
    doi: 10.1016/j.jveb.2011.10.007google scholar: lookup
  2. Górecka-Bruzda A., Kosińska I., Jaworski Z., Jezierski T., Murphy J.. Conflict behavior in elite show jumping and dressage horses.. J. Vet. Behav. 2015;10:137–146.
    doi: 10.1016/j.jveb.2014.10.004google scholar: lookup
  3. Munsters C.C., van Iwaarden A., van Weeren R., van Oldruitenborgh-Oosterbaan M.M.S.. Exercise testing in Warmblood sport horses under field conditions.. Vet. J. 2014;202:11–19.
    doi: 10.1016/j.tvjl.2014.07.019pubmed: 25172838google scholar: lookup
  4. Clayton H.M.. Comparison of the collected, working, medium and extended canters.. Equine Vet. J. 1994;26:16–19.
    doi: 10.1111/j.2042-3306.1994.tb04866.xpubmed: 8542844google scholar: lookup
  5. Casella E., Khamesi A.R., Silvestri S.. A framework for the recognition of horse gaits through wearable devices.. Pervasive Mob. Comput. 2020;67:101213.
    doi: 10.1016/j.pmcj.2020.101213google scholar: lookup
  6. Maisonpierre I., Sutton M., Harris P., Menzies-Gow N., Weller R., Pfau T.. Accelerometer activity tracking in horses and the effect of pasture management on time budget.. Equine Vet. J. 2019;51:840–845.
    doi: 10.1111/evj.13130pubmed: 31009100google scholar: lookup
  7. Bragança F.S., Broomé S., Rhodin M., Björnsdóttir S., Gunnarsson V., Voskamp J., 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
  8. Williams J., Perlo M., Marlin D.. A preliminary analysis of factors that result in faults in amateur (90–120 cm) showjumping.. J. Equine Vet. Sci. 2019;76:59–60.
    doi: 10.1016/j.jevs.2019.03.060google scholar: lookup
  9. Clayton H.M.. Comparison of the stride kinematics of the collected, working, medium and extended trot in horses.. Equine Vet. J. 1994;26:230–234.
    doi: 10.1111/j.2042-3306.1994.tb04375.xpubmed: 8542844google scholar: lookup
  10. Clayton H.M.. Classification of collected trot, passage and piaffe based on temporal variables.. Equine Vet. J. 1997;29:54–57.
    doi: 10.1111/j.2042-3306.1997.tb05054.xpubmed: 9354290google scholar: lookup
  11. Eerdekens A., Deruyck M., Fontaine J., Martens L., De Poorter E., Joseph W.. Automatic equine activity detection by convolutional neural networks using accelerometer data.. Comput. Electron. Agric. 2020;168:105139.
    doi: 10.1016/j.compag.2019.105139google scholar: lookup
  12. Williams J.M.. Electromyography in the horse: A useful technology?. J. Equine Vet. Sci. 2018;60:43–58.
    doi: 10.1016/j.jevs.2017.02.005google scholar: lookup
  13. Prochno H.C., Barussi F.M., Bastos F.Z., Weber S.H., Bechara G.H., Rehan I.F., Michelotto P.V.. Infrared thermography applied to monitoring musculoskeletal adaptation to training in Thoroughbred race horses.. J. Equine Vet. Sci. 2020;87:102935.
    doi: 10.1016/j.jevs.2020.102935pubmed: 32172920google scholar: lookup
  14. Eerdekens A., Deruyck M., Fontaine J., Martens L., De Poorter E., Plets D., Joseph W.. A framework for energy-efficient equine activity recognition with leg accelerometers.. Comput. Electron. Agric. 2021;183:106020.
    doi: 10.1016/j.compag.2021.106020google scholar: lookup
  15. Burla J.B., Ostertag A., Westerath H.S., Hillmann E.. Gait determination and activity measurement in horses using an accelerometer.. Comput. Electron. Agric. 2014;102:127–133.
    doi: 10.1016/j.compag.2014.01.001google scholar: lookup
  16. Schmutz A., Chèze L., Jacques J., Martin P.. A method to estimate horse speed per stride from one IMU with a machine learning method.. Sensors 2020;20:518.
    doi: 10.3390/s20020518pmc: PMC7014525pubmed: 31963422google scholar: lookup
  17. 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
  18. 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
  19. Clayton H.M.. Comparison of the stride kinematics of the collected, medium, and extended walks in horses.. Am. J. Vet. Res. 1995;56:849–852.
    pubmed: 7574149
  20. Calvert D., Bajcar E., Stacey D., Thomason J.. Analysis of equine gait through strain measurement. Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No. 03CH37439); Cancun, Mexico. 17–21 September 2003; pp. 2370–2373.
  21. Savelberg H., Van Loon T., Schamhardt H.. Ground reaction forces in horses, assessed from hoof wall deformation using artificial neural networks.. Equine Vet. J. 1997;29:6–8.
    doi: 10.1111/j.2042-3306.1997.tb05041.xpubmed: 9354277google scholar: lookup
  22. Mouloodi S., Rahmanpanah H., Burvill C., Davies H.M.. Prediction of load in a long bone using an artificial neural network prediction algorithm.. J. Mech. Behav. Biomed. Mater. 2020;102:103527.
    doi: 10.1016/j.jmbbm.2019.103527pubmed: 31879267google scholar: lookup
  23. Rahmanpanah H., Mouloodi S., Burvill C., Gohari S., Davies H.M.. Prediction of load-displacement curve in a complex structure using artificial neural networks: A study on a long bone.. Int. J. Eng. Sci. 2020;154:103319.
    doi: 10.1016/j.ijengsci.2020.103319google scholar: lookup
  24. . GEOPAT Polyflakes.. 2021.
  25. Internationale F.E.. Dressage Rules 25th Edition.. 2021.
  26. McGreevy P.. Equine Behavior.. W.B. Saunders; Oxford, UK: 2004. Glossary; pp. 351–356.
    doi: 10.1016/B978-0-7020-2634-8.50022-8google scholar: lookup
  27. Axivity. Axivity AX6 Accelerometer.. 2019.
  28. Max Planck Institute for Psycholinguistics. The Language Archive, N.T.N. ELAN.. .
  29. Brugman H., Russel A., Nijmegen X.. Annotating Multi-media/Multi-modal Resources with ELAN. Proceedings of the LREC 2004 (Fourth International Conference on Language Resources and Evaluation); Lisbon, Portugal. 26–28 May 2004.
  30. Liebal K., Waller B.M., Slocombe K.E., Burrows A.M.. Primate Communication: A Multimodal Approach.. Cambridge University Press; Cambridge, UK: 2014.
  31. Jeong C.Y., Kim M.. An energy-efficient method for human activity recognition with segment-level change detection and deep learning.. Sensors 2019;19:3688.
    doi: 10.3390/s19173688pmc: PMC6749525pubmed: 31450654google scholar: lookup
  32. Abadi M., Barham P., Chen J., Chen Z., Davis A., Dean J., Devin M., Ghemawat S., Irving G., Isard M.. Tensorflow: A system for large-scale machine learning. Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16); Savannah, GA, USA. 2–4 November 2016; pp. 265–283.
  33. Pedregosa F., Varoquaux G., Gramfort A., Michel V., Thirion B., Grisel O., Blondel M., Prettenhofer P., Weiss R., Dubourg V.. Scikit-learn: Machine learning in Python.. J. Mach. Learn. Res. 2011;12:2825–2830.
  34. Benaissa S., Tuyttens F.A., Plets D., Cattrysse H., Martens L., Vandaele L., Joseph W., Sonck B.. Classification of ingestive-related cow behaviours using RumiWatch halter and neck-mounted accelerometers.. Appl. Anim. Behav. Sci. 2019;211:9–16.
    doi: 10.1016/j.applanim.2018.12.003google scholar: lookup
  35. Le Roux S.P., Marias J., Wolhuter R., Niesler T.. Animal-borne behaviour classification for sheep (Dohne Merino) and Rhinoceros (Ceratotherium simum and Diceros bicornis). Anim. Biotelemetry. 2017;5:25.
    doi: 10.1186/s40317-017-0140-0google scholar: lookup
  36. Xing H., Li J., Hou B., Zhang Y., Guo M.. Pedestrian stride length estimation from IMU measurements and ANN based algorithm.. J. Sens. 2017;2017.
    doi: 10.1155/2017/6091261google scholar: lookup
  37. Tang M., Xia L., Wei D., Yan S., Du C., Cui H.L.. Distinguishing different cancerous human cells by raman spectroscopy based on discriminant analysis methods.. Appl. Sci. 2017;7:900.
    doi: 10.3390/app7090900google scholar: lookup
  38. Ruuska S., Hämäläinen W., Kajava S., Mughal M., Matilainen P., Mononen J.. Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle.. Behav. Process. 2018;148:56–62.
    doi: 10.1016/j.beproc.2018.01.004pubmed: 29330090google scholar: lookup
  39. Martini A., Rivola A., Troncossi M.. Autocorrelation analysis of vibro-acoustic signals measured in a test field for water leak detection.. Appl. Sci. 2018;8:2450.
    doi: 10.3390/app8122450google scholar: lookup

Citations

This article has been cited 5 times.
  1. Portier K, Schiesari C, Gauthier L, Yeng LT, Tabacchi Fantoni D, Formenton MR. Comparison of the Prevalence and Location of Trigger Points in Dressage and Show-Jumping Horses. Animals (Basel) 2025 May 27;15(11).
    doi: 10.3390/ani15111558pubmed: 40509024google scholar: lookup
  2. Schampheleer J, Eerdekens A, Joseph W, Martens L, Deruyck M. Detecting Equine Gaits Through Rider-Worn Accelerometers. Animals (Basel) 2025 Apr 8;15(8).
    doi: 10.3390/ani15081080pubmed: 40281916google scholar: lookup
  3. Siegers E, van Wijk E, van den Broek J, Sloet van Oldruitenborgh-Oosterbaan M, Munsters C. Longitudinal Training and Workload Assessment in Young Friesian Stallions in Relation to Fitness: Part 1. Animals (Basel) 2023 Feb 16;13(4).
    doi: 10.3390/ani13040689pubmed: 36830476google scholar: lookup
  4. Davíðsson HB, Rees T, Ólafsdóttir MR, Einarsson H. Efficient Development of Gait Classification Models for Five-Gaited Horses Based on Mobile Phone Sensors. Animals (Basel) 2023 Jan 3;13(1).
    doi: 10.3390/ani13010183pubmed: 36611791google scholar: lookup
  5. Gonçalves P, Pedreiras P, Monteiro A. Recent Advances in Smart Farming. Animals (Basel) 2022 Mar 11;12(6).
    doi: 10.3390/ani12060705pubmed: 35327101google scholar: lookup
Subscribe to our newsletter
Join 99,357 horse owners like you who receive our email updates on horse health and nutrition.