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PloS one2023; 18(4); e0284554; doi: 10.1371/journal.pone.0284554

Detecting fatigue of sport horses with biomechanical gait features using inertial sensors.

Abstract: Detection of fatigue helps prevent injuries and optimize the performance of horses. Previous studies tried to determine fatigue using physiological parameters. However, measuring the physiological parameters, e.g., plasma lactate, is invasive and can be affected by different factors. In addition, the measurement cannot be done automatically and requires a veterinarian for sample collection. This study investigated the possibility of detecting fatigue non-invasively using a minimum number of body-mounted inertial sensors. Using the inertial sensors, sixty sport horses were measured during walk and trot before and after high and low-intensity exercises. Then, biomechanical features were extracted from the output signals. A number of features were assigned as important fatigue indicators using neighborhood component analysis. Based on the fatigue indicators, machine learning models were developed for classifying strides to non-fatigue and fatigue. As an outcome, this study confirmed that biomechanical features can indicate fatigue in horses, such as stance duration, swing duration, and limb range of motion. The fatigue classification model resulted in high accuracy during both walk and trot. In conclusion, fatigue can be detected during exercise by using the output of body-mounted inertial sensors.
Copyright: © 2023 Darbandi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Publication Date: 2023-04-14 PubMed ID: 37058516PubMed Central: PMC10104328DOI: 10.1371/journal.pone.0284554Google 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 focuses on using body-mounted inertial sensors on sport horses to detect fatigue non-invasively. By observing changes in certain biomechanical features, such as swing and stance duration, and limb range of motion, the study found a high accuracy in predicting fatigue using machine learning models.

Objective and Background

  • The main objective of this study was to explore non-invasive fatigue detection technologies for sport horses, particularly utilizing minimum body-mounted inertial sensors. The importance of fatigue detection lies in its role in injury prevention and performance optimization.
  • Existing fatigue detection methods largely center on invasive physiological measures which require specialized assistance, frequently a veterinarian, making them less convenient and adaptable in real-world settings. These traditional methods, such as plasma lactate measurement, can also be influenced by various factors thus potentially affecting their accuracy.

Methodology

  • Sixty sport horses were assessed in a controlled setting, with the horses carrying out both low and high-intensity exercises.
  • Before and after these exercises, the inertial sensors were used to gather data on the horses’ walk and trot.
  • Crucial biomechanical features like limb range of motion, stance period, and swing duration were mined from the output signals of these sensors.
  • The neighborhood component analysis was applied to identify the most significant fatigue indicators from the biomechanical features identified.
  • A machine learning model was then built to classify strides into non-fatigue and fatigue, based on the indicators.

Results and Conclusion

  • The study affirmed the potential of biomechanical features to detect fatigue in sporting horses. Key fatigue indicating features identified included stance duration, swing duration, and limb range of motion.
  • A machine learning model was built next that resulted in high accuracy in classifying strides into non-fatigue and fatigue, in both walk and trot states.
  • The result of this study emphasizes the viability of a non-invasive, efficient, and automatic method for detecting fatigue in horses in real-time, using body-mounted inertial sensors.

Cite This Article

APA
Darbandi H, Munsters C, Parmentier J, Havinga P. (2023). Detecting fatigue of sport horses with biomechanical gait features using inertial sensors. PLoS One, 18(4), e0284554. https://doi.org/10.1371/journal.pone.0284554

Publication

PloS one
ISSN: 1932-6203
NlmUniqueID: 101285081
Country: United States
Language: English
Volume: 18
Issue: 4
Pages: e0284554
PII: e0284554

Researcher Affiliations

Darbandi, Hamed
  • Department of Computer Science, Pervasive Systems Group, University of Twente, Enschede, The Netherlands.
Munsters, Carolien
  • Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
  • Equine Integration, Hoogeloon, The Netherlands.
Parmentier, Jeanne
  • Department of Computer Science, Pervasive Systems Group, University of Twente, Enschede, The Netherlands.
  • Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
Havinga, Paul
  • Department of Computer Science, Pervasive Systems Group, University of Twente, Enschede, The Netherlands.

MeSH Terms

  • Horses
  • Animals
  • Gait / physiology
  • Walking / physiology
  • Extremities
  • Machine Learning
  • Biomechanical Phenomena

Conflict of Interest Statement

The authors have declared that no competing interests exist.

References

This article includes 55 references
  1. de Graaf-Roelfsema E, Keizer HA, van Breda E, Wijnberg ID, van der Kolk JH. Hormonal responses to acute exercise, training and overtraining. A review with emphasis on the horse.. Vet Q 2007 Sep;29(3):82-101.
    doi: 10.1080/01652176.2007.9695232pubmed: 17970286google scholar: lookup
  2. Takahashi Y, Mukai K, Matsui A, Ohmura H, Takahashi T. Electromyographic changes in hind limbs of Thoroughbreds with fatigue induced by treadmill exercise.. Am J Vet Res 2018 Aug;79(8):828-835.
    doi: 10.2460/ajvr.79.8.828pubmed: 30058845google scholar: lookup
  3. Munsters CCBM, Kingma BRM, van den Broek J, Sloet van Oldruitenborgh-Oosterbaan MM. A prospective cohort study on the acute:chronic workload ratio in relation to injuries in high level eventing horses: A comprehensive 3-year study.. Prev Vet Med 2020 Jun;179:105010.
    doi: 10.1016/j.prevetmed.2020.105010pubmed: 32447072google scholar: lookup
  4. Estberg L, Gardner IA, Stover SM, Johnson BJ. A case-crossover study of intensive racing and training schedules and risk of catastrophic musculoskeletal injury and lay-up in California thoroughbred racehorses.. Prev Vet Med 1998 Jan;33(1-4):159-70.
    doi: 10.1016/S0167-5877(97)00047-0pubmed: 9500171google scholar: lookup
  5. Hill AE, Gardner IA, Carpenter TE, Stover SM. Effects of injury to the suspensory apparatus, exercise, and horseshoe characteristics on the risk of lateral condylar fracture and suspensory apparatus failure in forelimbs of thoroughbred racehorses.. Am J Vet Res 2004 Nov;65(11):1508-17.
    doi: 10.2460/ajvr.2004.65.1508pubmed: 15566089google scholar: lookup
  6. Arfuso F, Giannetto C, Giudice E, Fazio F, Panzera M, Piccione G. Peripheral Modulators of the Central Fatigue Development and Their Relationship with Athletic Performance in Jumper Horses.. Animals (Basel) 2021 Mar 8;11(3).
    doi: 10.3390/ani11030743pmc: PMC8002136pubmed: 33800520google scholar: lookup
  7. Ropka-Molik K, Stefaniuk-Szmukier M, Szmatoła T, Piórkowska K, Bugno-Poniewierska M. The use of the SLC16A1 gene as a potential marker to predict race performance in Arabian horses.. BMC Genet 2019 Sep 11;20(1):73.
    doi: 10.1186/s12863-019-0774-4pmc: PMC6740031pubmed: 31510920google scholar: lookup
  8. Rivero JL, Serrano AL, Quiroz-Rothe E, Aguilera-Tejero E. Coordinated changes of kinematics and muscle fibre properties with prolonged endurance training.. Equine Vet J Suppl 2001 Apr;(33):104-8.
    doi: 10.1111/j.2042-3306.2001.tb05370.xpubmed: 11721547google scholar: lookup
  9. Hogg RC, Hodgins GA. Symbiosis or Sporting Tool? Competition and the Horse-Rider Relationship in Elite Equestrian Sports.. Animals (Basel) 2021 May 10;11(5).
    doi: 10.3390/ani11051352pmc: PMC8151029pubmed: 34068606google scholar: lookup
  10. Verheyen KL, Wood JL. Descriptive epidemiology of fractures occurring in British Thoroughbred racehorses in training.. Equine Vet J 2004 Mar;36(2):167-73.
    doi: 10.2746/0425164044868684pubmed: 15038441google scholar: lookup
  11. Hockey R. The psychology of fatigue: Work, effort and control.. Cambridge University Press 2013 May 16.
  12. Pattyn N, Van Cutsem J, Dessy E, Mairesse O. Bridging Exercise Science, Cognitive Psychology, and Medical Practice: Is "Cognitive Fatigue" a Remake of "The Emperor's New Clothes"?. Front Psychol 2018;9:1246.
    doi: 10.3389/fpsyg.2018.01246pmc: PMC6139359pubmed: 30250436google scholar: lookup
  13. Colborne GR, Birtles DM, Cacchione IC. Electromyographic and kinematic indicators of fatigue in horses: a pilot study.. Equine Vet J Suppl 2001 Apr;(33):89-93.
    doi: 10.1111/j.2042-3306.2001.tb05367.xpubmed: 11721578google scholar: lookup
  14. Warren LK, Lawrence LM, Thompson KN. The influence of betaine on untrained and trained horses exercising to fatigue.. J Anim Sci 1999 Mar;77(3):677-84.
    doi: 10.2527/1999.773677xpubmed: 10229364google scholar: lookup
  15. Takahashi Y, Mukai K, Ohmura H, Takahashi T. Do Muscle Activities of M. Splenius and M. Brachiocephalicus Decrease Because of Exercise-Induced Fatigue in Thoroughbred Horses?. J Equine Vet Sci 2020 Mar;86:102901.
    doi: 10.1016/j.jevs.2019.102901pubmed: 32067667google scholar: lookup
  16. Curtis RA, Kusano K, Evans DL. Observations on respiratory flow strategies during and after intense treadmill exercise to fatigue in thoroughbred racehorses.. Equine Vet J Suppl 2006 Aug;(36):567-72.
    doi: 10.1111/j.2042-3306.2006.tb05606.xpubmed: 17402485google scholar: lookup
  17. Kusano K, Curtis RA, Goldman CA, Evans DL. Relative Flow-Time Relationships in Single Breaths Recorded After Treadmill Exercise in Thoroughbred Horses. J Equine Vet Sci 2007 Aug;27(8):362–368.
    doi: 10.1016/j.jevs.2007.06.005google scholar: lookup
  18. Bayly W, Lopez C, Sides R, Bergsma G, Bergsma J, Gold J, Sellon D. Effect of different protocols on the mitigation of exercise-induced pulmonary hemorrhage in horses when administered 24 hours before strenuous exercise.. J Vet Intern Med 2019 Sep;33(5):2319-2326.
    doi: 10.1111/jvim.15574pmc: PMC6766505pubmed: 31397944google scholar: lookup
  19. Wickler SJ, Greene HM, Egan K, Astudillo A, Dutto DJ, Hoyt DF. Stride parameters and hindlimb length in horses fatigued on a treadmill and at an endurance ride.. Equine Vet J Suppl 2006 Aug;(36):60-4.
    doi: 10.1111/j.2042-3306.2006.tb05514.xpubmed: 17402393google scholar: lookup
  20. Ferrari M, Pfau T, Wilson AM, Weller R. The effect of training on stride parameters in a cohort of National Hunt racing Thoroughbreds: a preliminary study.. Equine Vet J 2009 May;41(5):493-7.
    doi: 10.2746/042516409X374591pubmed: 19642411google scholar: lookup
  21. Bowers JR, Slocombe RF. Influence of girth strap tensions on athletic performance of racehorses.. Equine Vet J Suppl 1999 Jul;(30):52-6.
    doi: 10.1111/j.2042-3306.1999.tb05188.xpubmed: 10659222google scholar: lookup
  22. Savage KA, Colahan PT, Tebbett IR, Rice BL, Freshwater LL, Jackson CA. Effects of caffeine on exercise performance of physically fit Thoroughbreds.. Am J Vet Res 2005 Apr;66(4):569-73.
    doi: 10.2460/ajvr.2005.66.569pubmed: 15900934google scholar: lookup
  23. Jose-Cunilleras E, Young LE, Newton JR, Marlin DJ. Cardiac arrhythmias during and after treadmill exercise in poorly performing thoroughbred racehorses.. Equine Vet J Suppl 2006 Aug;(36):163-70.
    doi: 10.1111/j.2042-3306.2006.tb05534.xpubmed: 17402413google scholar: lookup
  24. Buhl R, Carstensen H, Hesselkilde EZ, Klein BZ, Hougaard KM, Ravn KB, Loft-Andersen AV, Fenner MF, Pipper C, Jespersen T. Effect of induced chronic atrial fibrillation on exercise performance in Standardbred trotters.. J Vet Intern Med 2018 Jul;32(4):1410-1419.
    doi: 10.1111/jvim.15137pmc: PMC6060327pubmed: 29749082google scholar: lookup
  25. Munsters CC, van Iwaarden A, van Weeren R, Sloet van Oldruitenborgh-Oosterbaan MM. Exercise testing in Warmblood sport horses under field conditions.. Vet J 2014 Oct;202(1):11-9.
    doi: 10.1016/j.tvjl.2014.07.019pubmed: 25172838google scholar: lookup
  26. Darbandi H, Havinga P. A machine learning approach to analyze rider’s effects on horse gait using on-body inertial sensors. 2022 IEEE International Conference on Pervasive Computing and Communications Workshops .
  27. Baron B, Moullan F, Deruelle F, Noakes TD. The role of emotions on pacing strategies and performance in middle and long duration sport events.. Br J Sports Med 2011 May;45(6):511-7.
    doi: 10.1136/bjsm.2009.059964pubmed: 19553226google scholar: lookup
  28. Eto D, Yamano S, Mukai K, Sugiura T, Nasu T, Tokuriki M, Miyata H. Effect of high intensity training on anaerobic capacity of middle gluteal muscle in Thoroughbred horses.. Res Vet Sci 2004 Apr;76(2):139-44.
    doi: 10.1016/j.rvsc.2003.08.010pubmed: 14672857google scholar: lookup
  29. Piccione G, Messina V, Casella S, Giannetto C, Caola G. Blood lactate levels during exercise in athletic horses. Comp Clin Path 2010 Feb; 6(19): 535–539.
    doi: 10.1007/s00580-010-0965-xgoogle scholar: lookup
  30. Jansen F, Van der Krogt J, Van Loon K, Avezzù V, Guarino M, Quanten S, Berckmans D. Online detection of an emotional response of a horse during physical activity.. Vet J 2009 Jul;181(1):38-42.
    doi: 10.1016/j.tvjl.2009.03.017pubmed: 19375961google scholar: lookup
  31. Cottin F, Barrey E, Lopes P, Billat V. Effect of repeated exercise and recovery on heart rate variability in elite trotting horses during high intensity interval training.. Equine Vet J Suppl 2006 Aug;(36):204-9.
    doi: 10.1111/j.2042-3306.2006.tb05540.xpubmed: 17402419google scholar: lookup
  32. Williams JM, Jane M. Electromyography in the Horse: A Useful Technology?. J Equine Vet Sci 2018 Jan;(60):43–58.
    doi: 10.1016/j.jevs.2017.02.005google scholar: lookup
  33. Pugliese BR, Carballo CT, Connolly KM, Mazan MR, Kirker-Head CA. Effect of Fatigue on Equine Metacarpophalangeal Joint Kinematics-A Single Horse Pilot Study.. J Equine Vet Sci 2020 Mar;86:102849.
    doi: 10.1016/j.jevs.2019.102849pubmed: 32067670google scholar: lookup
  34. Takahashi Y, Takahashi T, Mukai K, Ohmura H. Effects of Fatigue on Stride Parameters in Thoroughbred Racehorses During Races.. J Equine Vet Sci 2021 Jun;101:103447.
    doi: 10.1016/j.jevs.2021.103447pubmed: 33993952google scholar: lookup
  35. Barrey E. Methods, applications and limitations of gait analysis in horses.. Vet J 1999 Jan;157(1):7-22.
    doi: 10.1053/tvjl.1998.0297pubmed: 10030124google scholar: lookup
  36. Parkes RSV, Weller R, Pfau T, Witte TH. The Effect of Training on Stride Duration in a Cohort of Two-Year-Old and Three-Year-Old Thoroughbred Racehorses.. Animals (Basel) 2019 Jul 22;9(7).
    doi: 10.3390/ani9070466pmc: PMC6680649pubmed: 31336595google scholar: lookup
  37. Darbandi H, Serra Bragança F, van der Zwaag BJ, Voskamp J, Gmel AI, Haraldsdóttir EH, Havinga P. Using Different Combinations of Body-Mounted IMU Sensors to Estimate Speed of Horses-A Machine Learning Approach.. Sensors (Basel) 2021 Jan 26;21(3).
    doi: 10.3390/s21030798pmc: PMC7865839pubmed: 33530288google scholar: lookup
  38. Bosch S, Serra Bragança F, Marin-Perianu M, Marin-Perianu R, van der Zwaag BJ, Voskamp J, Back W, van Weeren R, Havinga P. EquiMoves: A Wireless Networked Inertial Measurement System for Objective Examination of Horse Gait.. Sensors (Basel) 2018 Mar 13;18(3).
    doi: 10.3390/s18030850pmc: PMC5877382pubmed: 29534022google scholar: lookup
  39. Darbandi H, Serra Bragança F, van der Zwaag BJ, Havinga P. Accurate Horse Gait Event Estimation Using an Inertial Sensor Mounted on Different Body Locations. 2022 IEEE International Conference on Smart Computing Workshops (SMARTCOMP) .
  40. Allen K J, van Erck-Westergren E, Franklin S H. Exercise testing in the equine athlete. Equine Veterinary Education 2016;28(2):89.
    doi: 10.1111/eve.12410google scholar: lookup
  41. Valenti RG, Dryanovski I, Xiao J. Keeping a Good Attitude: A Quaternion-Based Orientation Filter for IMUs and MARGs.. Sensors (Basel) 2015 Aug 6;15(8):19302-30.
    doi: 10.3390/s150819302pmc: PMC4570372pubmed: 26258778google scholar: lookup
  42. Pfau T, Witte TH, Wilson AM. A method for deriving displacement data during cyclical movement using an inertial sensor.. J Exp Biol 2005 Jul;208(Pt 13):2503-14.
    doi: 10.1242/jeb.01658pubmed: 15961737google scholar: lookup
  43. Kramer J, Keegan KG, Kelmer G, Wilson DA. Objective determination of pelvic movement during hind limb lameness by use of a signal decomposition method and pelvic height differences.. Am J Vet Res 2004 Jun;65(6):741-7.
    doi: 10.2460/ajvr.2004.65.741pubmed: 15198212google scholar: lookup
  44. Wight JT, Garman JEJ, Hooper DR, Robertson CT, Ferber R, Boling MC. Distance running stride-to-stride variability for sagittal plane joint angles.. Sports Biomech 2022 Sep;21(8):966-980.
    pubmed: 32129719doi: 10.1080/14763141.2020.1713207google scholar: lookup
  45. Nauwelaerts S, Aerts P, Clayton H. Stride to stride variability in joint angle profiles during transitions from trot to canter in horses.. Vet J 2013 Dec;198 Suppl 1:e59-64.
    doi: 10.1016/j.tvjl.2013.09.034pubmed: 24314716google scholar: lookup
  46. Burdack J, Horst F, Aragonés D, Eekhoff A, Schöllhorn WI. Fatigue-Related and Timescale-Dependent Changes in Individual Movement Patterns Identified Using Support Vector Machine.. Front Psychol 2020;11:551548.
    doi: 10.3389/fpsyg.2020.551548pmc: PMC7554555pubmed: 33101124google scholar: lookup
  47. Estep A, Morrison S, Caswell S, Ambegaonkar J, Cortes N. Differences in pattern of variability for lower extremity kinematics between walking and running.. Gait Posture 2018 Feb;60:111-115.
    doi: 10.1016/j.gaitpost.2017.11.018pubmed: 29179051google scholar: lookup
  48. Nohelova D, Bizovska L, Vuillerme N, Svoboda Z. Gait Variability and Complexity during Single and Dual-Task Walking on Different Surfaces in Outdoor Environment.. Sensors (Basel) 2021 Jul 14;21(14).
    doi: 10.3390/s21144792pmc: PMC8309897pubmed: 34300532google scholar: lookup
  49. Goldberger J, Roweis S, Hinton G, Salakhutdinov R. Neighbourhood Components Analysis. NIPS’04: Proceedings of the 17th International Conference on Neural Information Processing Systems 2004 Dec;513-520.
  50. Varma S, Simon R. Bias in error estimation when using cross-validation for model selection.. BMC Bioinformatics 2006 Feb 23;7:91.
    doi: 10.1186/1471-2105-7-91pmc: PMC1397873pubmed: 16504092google scholar: lookup
  51. Abourachid A. A new way of analysing symmetrical and asymmetrical gaits in quadrupeds.. C R Biol 2003 Jul;326(7):625-30.
    doi: 10.1016/S1631-0691(03)00170-7pubmed: 14556381google scholar: lookup
  52. Johnston C, Gottlieb-Vedi M, Drevemo S, Roepstorff L. The kinematics of loading and fatigue in the standardbred trotter.. Equine Vet J Suppl 1999 Jul;(30):249-53.
    doi: 10.1111/j.2042-3306.1999.tb05228.xpubmed: 10659262google scholar: lookup
  53. Zhang J, Lockhart TE, Soangra R. Classifying lower extremity muscle fatigue during walking using machine learning and inertial sensors.. Ann Biomed Eng 2014 Mar;42(3):600-12.
    doi: 10.1007/s10439-013-0917-0pmc: PMC3943497pubmed: 24081829google scholar: lookup
  54. Baghdadi A, Megahed FM, Esfahani ET, Cavuoto LA. A machine learning approach to detect changes in gait parameters following a fatiguing occupational task.. Ergonomics 2018 Aug;61(8):1116-1129.
    doi: 10.1080/00140139.2018.1442936pubmed: 29452575google scholar: lookup
  55. Harris P, Snow DH. The effects of high intensity exercise on the plasma concentration of lactate, potassium and other electrolytes.. Equine Vet J 1988 Mar;20(2):109-13.
    doi: 10.1111/j.2042-3306.1988.tb01470.xpubmed: 3371312google scholar: lookup

Citations

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    doi: 10.3390/vetsci13010009pubmed: 41600665google scholar: lookup
  2. Saitua A, Pérez-Umbría J, García-Álamo K, Muñoz A. Dynamic Mobilization Exercises Improve Activity and Stride Parameters Measured with Accelerometry in Sedentary Horses. Animals (Basel) 2025 Oct 10;15(20).
    doi: 10.3390/ani15202943pubmed: 41153872google scholar: lookup
  3. Siegers EW, Parmentier JIM, Sloet van Oldruitenborgh-Oosterbaan MM, Munsters CCBM, Serra Bragança FM. Gait kinematics at trot before and after repeated ridden exercise tests in young Friesian stallions during a fatiguing 10-week training program. Front Vet Sci 2025;12:1456424.
    doi: 10.3389/fvets.2025.1456424pubmed: 39995550google scholar: lookup
  4. Bogossian PM, Nattala U, Wong ASM, Morrice-West AV, Zhang GZ, Rana P, Whitton RC, Hitchens PL. A machine learning approach to identify stride characteristics predictive of musculoskeletal injury, enforced rest and retirement in Thoroughbred racehorses. Sci Rep 2024 Nov 22;14(1):28967.
    doi: 10.1038/s41598-024-79071-1pubmed: 39578597google scholar: lookup
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