Analyze Diet
Equine veterinary journal2025; doi: 10.1111/evj.70092

Technologies for equine welfare and performance monitoring under field conditions – Where do we stand?

Abstract: The need for comprehensive equine welfare assessments has become particularly evident amid ongoing debates about the social licence to operate in equestrian sports. During exercise, multiple physiological systems, principally the cardiovascular, respiratory, muscular, thermoregulatory, endocrine, and locomotory systems, undergo complex adaptations. To monitor and determine equine welfare, an approach that focuses on the quantitative monitoring of both physiological and psychological parameters to determine and understand the impact of equestrian sports on horses is essential. Existing and emerging technologies that allow for the quantitative assessment of such parameters have developed rapidly over the past two decades and have increasingly enabled precise monitoring of horses, though the available tools vary depending on the parameter of interest. This review explores current technologies for measuring parameters associated with these physiological systems and their practical applications in assessing equine well-being. The focus lies on validated technologies for which accuracy and precision have been determined. The aim of this review is to present an overview of current technologies available for the measurement of both physiological and psychological parameters in horses during exercise and to what extent they can be used under field conditions. The review concludes by discussing promising innovations that, while still in early development, could significantly contribute to equine welfare and the broader social licence to operate debate.
Publication Date: 2025-09-06 PubMed ID: 40913481DOI: 10.1111/evj.70092Google 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.
  • Journal Article
  • Review

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.

Overview

  • This research article reviews the current and emerging technologies used to monitor the welfare and performance of horses during exercise in real-world field conditions.
  • It highlights validated tools that measure physiological and psychological parameters to assess equine well-being, while also discussing future innovations that could improve welfare monitoring and support the social acceptability of equestrian sports.

Introduction and Background

  • The need for comprehensive equine welfare assessments has become more urgent due to societal debates about the ethics and social licence of equestrian sports.
  • During exercise, horses undergo complex physiological changes involving multiple systems:
    • Cardiovascular system
    • Respiratory system
    • Muscular system
    • Thermoregulatory system
    • Endocrine system
    • Locomotory system
  • Monitoring these systems quantitatively allows objective assessment of a horse’s welfare and helps understand the impact of sports activities.

Purpose of the Review

  • To provide an overview of current technologies available for measuring physiological and psychological parameters in horses during exercise.
  • To focus primarily on validated tools where accuracy and precision have been demonstrated.
  • To address the extent to which these technologies can be applied under field conditions, i.e., outside laboratory environments.
  • To examine the practical applications of these technologies for assessing equine welfare.
  • To discuss promising innovative technologies that are still in development but hold potential to advance welfare monitoring.

Technologies for Physiological Monitoring

  • Cardiovascular Monitoring:
    • Heart rate monitors and ECG devices used to track cardiac functioning.
    • Validated devices provide accurate heart rate and rhythm readings during exercise.
    • Useful for assessing stress, fitness levels, and exertion intensity.
  • Respiratory Monitoring:
    • Technology measures breathing rate, tidal volume, and respiratory patterns.
    • Wearable respiratory sensors are being developed suitable for field use.
    • Important for detecting respiratory distress or fatigue.
  • Muscular and Locomotory Systems:
    • Accelerometers, gyroscopes, and motion capture technologies assess gait, stride, and muscular function.
    • These tools help detect asymmetries, lameness, or inefficient movement patterns.
    • Some systems are ruggedized for outdoor conditions enabling continuous monitoring.
  • Thermoregulatory and Endocrine Monitoring:
    • Core and skin temperature sensors monitor thermal regulation performance.
    • Hormone level measurement technologies, often involving blood or saliva samples, reveal stress and metabolic status.
    • Non-invasive or minimally invasive methods are an area of ongoing research.

Technologies for Psychological Monitoring

  • Psychological parameters such as stress, pain, or anxiety are more challenging to quantify.
  • Behavioral monitoring via video analysis and automated motion tracking provides insights into discomfort or distress.
  • Heart rate variability (HRV) measures autonomic nervous system balance, serving as an indicator of emotional state.
  • Emergent sensor technologies aim to integrate physiological and behavioral data for comprehensive welfare assessment.

Field Applicability of Technologies

  • The review emphasizes tools validated for accuracy in real-world field conditions, not just controlled lab environments.
  • Challenges for field deployment include:
    • Durability and robustness against weather and movement.
    • Ease of use by trainers, vets, or riders without specialized equipment.
    • Wireless data transmission and real-time analysis capabilities.
  • Current technologies are increasingly portable, user-friendly, and precise, facilitating on-site welfare monitoring.

Future Innovations and Potential Impact

  • Several emerging technologies under development may transform welfare monitoring, such as:
    • Advanced biosensors for real-time hormone or metabolite detection.
    • Artificial intelligence-driven behavioral analysis platforms.
    • Integrative systems combining multiple sensor data streams for comprehensive health profiles.
  • These innovations could improve early detection of welfare issues and help maintain the social licence to operate equestrian sports.
  • Increased welfare transparency and objective measurement tools may positively influence public perception and regulatory policies.

Conclusions

  • The review concludes that while significant progress has been made in equine welfare monitoring technologies, there is no one-size-fits-all solution.
  • Technologies vary based on the parameter monitored, with some ready for broad field use and others still experimental.
  • Ongoing development and validation are critical to ensure tools are accurate, practical, and sensitive to subtle welfare indicators.
  • Integration of physiological and psychological data streams offers the best pathway toward comprehensive equine welfare assessment.
  • Such advancements hold promise for enhancing performance, health, and ethical standards in equestrian sports.

Cite This Article

APA
Aarts RM, Siegers EW, Serra Braganca FM, van Weeren PR. (2025). Technologies for equine welfare and performance monitoring under field conditions – Where do we stand? Equine Vet J. https://doi.org/10.1111/evj.70092

Publication

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

Researcher Affiliations

Aarts, Rhana Mackie
  • Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
Siegers, Esther W
  • Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
Serra Braganca, Filipe M
  • Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.
van Weeren, P René
  • Department of Clinical Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, The Netherlands.

Grant Funding

  • E!114697 / Eurostars

References

This article includes 153 references
  1. Skinner JE, Hilly LJ, Li X, Cawdell‐Smith AJ, Bryden WL. Equine production systems and the changing role of horses in society. New York: Nova Science Publishers; 2019. p. 389–433.
  2. McGreevy P, Berger J, Brauwere ND, de Brauwere N, Doherty O, Harrison A. Using the five domains model to assess the adverse impacts of husbandry, veterinary, and equitation interventions on horse welfare. Animals 2018;8(3):41.
    doi: 10.3390/ani8030041google scholar: lookup
  3. Mellor D, Burns M. Using the Five Domains Model to develop welfare assessment guidelines for Thoroughbred horses in New Zealand. N Z Vet J 2020;68(3):150–156.
  4. Arndt SS, Goerlich VC, van der Staay FJ. A dynamic concept of animal welfare: the role of appetitive and adverse internal and external factors and the animal's ability to adapt to them. Front Anim Sci 2022;3:908513.
    doi: 10.3389/fanim.2022.908513google scholar: lookup
  5. Mellor DJ, Beausoleil NJ. Extending the ‘five domains’ model for animal welfare assessment to incorporate positive welfare states. Anim Welf 2015;24(3):241–253.
    doi: 10.7120/09627286.24.3.241google scholar: lookup
  6. Hockenhull J, Whay HR. A review of approaches to assessing equine welfare. Equine Vet Educ 2014;26(3):159–166.
    doi: 10.1111/eve.12129google scholar: lookup
  7. Douglas J, Owers R, Campbell MLH. Social licence to operate: what can equestrian sports learn from other industries?. Animals 2022;12(15):1987.
    doi: 10.3390/ani12151987google scholar: lookup
  8. Heleski C, Stowe CJ, Fiedler J, Peterson ML, Brady C, Wickens C. Thoroughbred racehorse welfare through the lens of social license to operate‐with an emphasis on a U.S. perspective. Sustainability 2020;12(5):1706.
    doi: 10.3390/su12051706google scholar: lookup
  9. Campbell MLH. Freedoms and frameworks: how we think about the welfare of competition horses. Equine Vet J 2016;48(5):540–542.
    doi: 10.1111/evj.12598google scholar: lookup
  10. Campbell MLH. An ethical framework for the use of horses in competitive sport: theory and function. Animals 2021;11(6):1725.
    doi: 10.3390/ani11061725google scholar: lookup
  11. Annan R, Trigg LE, Hockenhull J, Allen K, Butler D, Valenchon M. Racehorse welfare across a training season. Front Vet Sci 2023;10:10.
  12. Hedman FL, Rodriguez Ewerlöf I, Frössling J, Berg C. Swedish trotting horse trainers' perceptions of animal welfare inspections from public and private actors. Animals 2022;12(11):1–8.
    doi: 10.3390/ani1211google scholar: lookup
  13. Lundmark Hedman F, Rodriguez Ewerlöf I, Frössling J, Berg C. Swedish trotting horse trainers' perceptions of animal welfare inspections from public and private actors. Animals 2022;12(11):1441.
    doi: 10.3390/ani12111441google scholar: lookup
  14. Holmes TQ, Brown AF. Champing at the bit for improvements: a review of equine welfare in equestrian sports in the United Kingdom. Animals 2022;12(9):1186.
    doi: 10.3390/ani12091186google scholar: lookup
  15. Voigt MA, Hiney K, Richardson JC, Waite K, Borron A, Brady CM. Show horse welfare: horse show competitors' understanding, awareness, and perceptions of equine welfare. J Appl Anim Welf Sci 2016;19:335–352.
  16. Luke KL, Rawluk A, McAdie T, Smith BP, Warren‐Smith AK. How equestrians conceptualise horse welfare: does it facilitate or hinder change?. Anim Welf 2023;32:e59.
    doi: 10.1017/awf.2023.79google scholar: lookup
  17. Robinson IH. The human‐horse relationship: how much do we know?. Equine Vet J 1999;31(S28):42–45.
  18. Furtado T, Preshaw L, Hockenhull J, Wathan J, Douglas J, Horseman S. How happy are equine athletes? Stakeholder perceptions of equine welfare issues associated with equestrian sport. Animals 2021;11(11):3228.
    doi: 10.3390/ani11113228google scholar: lookup
  19. Hinchcliff K, Geor R. The horse as an athlete: a physiological overview. Equine exercise physiology Edinburgh: Elsevier; 2008. p. 2–11.
  20. Darbandi H, Munsters C, Parmentier J, Havinga P. Detecting fatigue of sport horses with biomechanical gait features using inertial sensors. PLoS One 2023;18(4):e0284554.
  21. Parmentier JIM, Bosch S, Van Der Zwaag BJ, Weishaupt MA, Gmel AI, Havinga PJM. Prediction of continuous and discrete kinetic parameters in horses from inertial measurement units data using recurrent artificial neural networks. Sci Rep 2023;13(1):740.
  22. McLean AN, McGreevy PD. Ethical equitation: capping the price horses pay for human glory. J Vet Behav 2010;5(4):203–209.
  23. Navas de Solis C. Cardiovascular response to exercise and training, exercise testing in horses. Vet Clin N Am – Equine Pract 2019;35(1):159–173.
  24. Navas De Solis C, Ramseyer A, Stefanovski D, Haughan J, Solomon CJ, Kirsch K. Association of heart rate variability, exercise intensity and exercising arrhythmias with competition results in eventing horses. Equine Vet J 2025; 1–11.
    doi: 10.1111/evj.14491google scholar: lookup
  25. Stucke D, Große Ruse M, Lebelt D. Measuring heart rate variability in horses to investigate the autonomic nervous system activity – pros and cons of different methods. Appl Anim Behav Sci 2015;166:1–10.
  26. Frick L, Schwarzwald CC, Mitchell KJ. The use of heart rate variability analysis to detect arrhythmias in horses undergoing a standard treadmill exercise test. J Vet Intern Med 2019;33(1):212–224.
    doi: 10.1111/jvim.15358google scholar: lookup
  27. Brložnik M, Domanjko Petrič A, Kadunc Kos V, Rashkovska A, Avbelj V. Wireless Body Sensor for Electrocardiographic Monitoring in Equine Medicine. 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO) New York: IEEE; 2019.
  28. Lanata A, Guidi A, Baragli P, Valenza G, Scilingo EP. A novel algorithm for movement artifact removal in ECG signals acquired from wearable systems applied to horses. PLoS One 2015;10(10):e0140783.
  29. McCrae P, Spong H, Rutherford AA, Osborne V, Mahnam A, Pearson W. A smart textile band achieves high‐quality electrocardiograms in unrestrained horses. Animals 2022;12(23):3254.
    doi: 10.3390/ani12233254google scholar: lookup
  30. McCrae P, Spong H, Golestani N, Mahnam A, Bashura Y, Pearson W. Validation of an equine smart textile system for heart rate variability: a preliminary study. Animals 2023;13(3):512.
    doi: 10.3390/ani13030512google scholar: lookup
  31. Guidi A, Baragli P, Lanata A, Paradiso R, Valenza G, Scilingo EP. Removing movement artifacts from equine ECG recordings acquired with textile electrodes. Annu Int Conf IEEE Eng Med Biol Soc 2015;2015:1955–1958.
  32. Marr CM, Bowen IM, editors. Cardiology of the horse. 2nd ed. Edinburgh: Saunders; 2010.
  33. Kapteijn CM, Frippiat T, Van Beckhoven C, van Lith HA, Endenburg N, Vermetten E. Measuring heart rate variability using a heart rate monitor in horses (Equus caballus) during groundwork. Front Vet Sci 2022;9:939534.
    doi: 10.3389/fvets.2022.939534google scholar: lookup
  34. McDuffee L, Mills M, McNiven M, Montelpare W. Establishing statistical stability for heart rate variability in horses. J Vet Behav 2019;32:30–35.
  35. Mott R, Dowell F, Evans N. Use of the polar V800 and Actiheart 5 heart rate monitors for the assessment of heart rate variability (HRV) in horses. Appl Anim Behav Sci 2021;241:105401.
  36. Parker M, Goodwin D, Eager RA, Redhead ES, Marlin DJ. Comparison of Polar® heart rate interval data with simultaneously recorded ECG signals in horses. Comp Exerc Physiol 2009;6(4):137–142.
    doi: 10.1017/s1755254010000024google scholar: lookup
  37. Frippiat T, Van Beckhoven C, Moyse E, Art T. Accuracy of a heart rate monitor for calculating heart rate variability parameters in exercising horses. J Equine Vet Sci 2021;104:103716.
  38. Ter Woort F, Dubois G, Didier M, Van Erck‐Westergren E. Validation of an equine fitness tracker: heart rate and heart rate variability. Comp Exerc Physiol 2021;17(2):189–198.
    doi: 10.3920/cep200028google scholar: lookup
  39. Ter Woort F, Dubois G, Tansley G, Didier M, Verdegaal L, Franklin S. Validation of an equine fitness tracker: ECG quality and arrhythmia detection. Equine Vet J 2023;55(2):336–343.
    doi: 10.1111/evj.13565google scholar: lookup
  40. Guidi A, Lanata A, Baragli P, Valenza G, Scilingo EP. A wearable system for the evaluation of the human–horse interaction: a preliminary study. Electronics 2016;5(4):63.
  41. Felici M, Nardelli M, Lanatà A, Sgorbini M, Pasquale Scilingo E, Baragli P. Smart textiles biotechnology for electrocardiogram monitoring in horses during exercise on treadmill: validation tests. Equine Vet J 2021;53(2):373–378.
    doi: 10.1111/evj.13296google scholar: lookup
  42. Spitale D, Cicogna M, Porciello F, Colonnelli M, Caivano D. Use of a novel smartphone based single‐lead electrocardiogram device in clinical practice to evaluate heart rhythm in horses – a case series. Pferdeheilkd Equine Med 2024;40(2):149–153.
    doi: 10.21836/pem20240207google scholar: lookup
  43. Vitale V, Vezzosi T, Tognetti R, Fraschetti C, Sgorbini M. Evaluation of a new portable 1‐lead digital cardiac monitor (eKuore) compared with standard base‐apex electrocardiography in healthy horses. PLoS One 2021;16(8):e0255247.
  44. Kraus MS, Rishniw M, Divers TJ, Reef VB, Gelzer AR. Utility and accuracy of a smartphone‐based electrocardiogram device as compared to a standard base‐apex electrocardiogram in the horse. Res Vet Sci 2019;125:141–147.
  45. Vezzosi T, Sgorbini M, Bonelli F, Buralli C, Pillotti M, Meucci V. Evaluation of a smartphone electrocardiograph in healthy horses: comparison with standard base‐apex electrocardiography. J Equine Vet Sci 2018;67:61–65.
  46. Welch‐Huston B, Durward‐Akhurst S, Norton E, Ellingson L, Rendahl A, McCue M. Comparison between smartphone electrocardiography and standard three‐lead base apex electrocardiography in healthy horses. Vet Rec 2020;187(9):e70.
    doi: 10.1136/vr.105759google scholar: lookup
  47. Roepstorff C, Dittmann MT, Arpagaus S, Serra Bragança FM, Hardeman A, Persson‐Sjödin E. Reliable and clinically applicable gait event classification using upper body motion in walking and trotting horses. J Biomech 2021;114:110146.
  48. Bosch S, Serra Bragança F, Marin‐Perianu M, Marin‐Perianu R, Van der Zwaag B, Voskamp J. Equimoves: a wireless networked inertial measurement system for objective examination of horse gait. Sensors 2018;18(3):850.
    doi: 10.3390/s18030850google scholar: lookup
  49. Bragança FM, Bosch S, Voskamp JP, Marin‐Perianu M, van der Zwaag BJ, Vernooij JCM. Validation of distal limb mounted inertial measurement unit sensors for stride detection in Warmblood horses at walk and trot. Equine Vet J 2017;49(4):545–551.
    doi: 10.1111/evj.12651google scholar: lookup
  50. Tijssen M, Hernlund E, Rhodin M, Bosch S, Voskamp JP, Nielen M. Automatic hoof‐on and ‐off detection in horses using hoof‐mounted inertial measurement unit sensors. PLoS One 2020;15(6):e0233266.
  51. Keegan KG, Yonezawa Y, Pai PF, Wilson DA, Kramer J. Evaluation of a sensor‐based system of motion analysis for detection and quantification of forelimb and hind limb lameness in horses. Am J Vet Res 2004;65(5):665–670.
    doi: 10.2460/ajvr.2004.65.665google scholar: lookup
  52. Pfau T, Witte TH, Wilson AM. A method for deriving displacement data during cyclical movement using an inertial sensor. J Exp Biol 2005;208(13):2503–2514.
    doi: 10.1242/jeb.01658google scholar: lookup
  53. Warner SM, Koch TO, Pfau T. Inertial sensors for assessment of back movement in horses during locomotion over ground. Equine Vet J 2010;42(S38):417–424.
  54. Guyard KC, Montavon S, Bertolaccini J, Deriaz M. Validation of Alogo move pro: a GPS‐based inertial measurement unit for the objective examination of gait and jumping in horses. Sensors 2023;23(9):4196.
    doi: 10.3390/s23094196google scholar: lookup
  55. 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.
    doi: 10.3390/ani13030390google scholar: lookup
  56. Dodson M. Shannon's sampling theorem. Curr Sci 1992;63(5):253–260.
  57. Flethøj M, Kanters JK, Pedersen PJ, Haugaard MM, Carstensen H, Olsen LH. Appropriate threshold levels of cardiac beat‐to‐beat variation in semi‐automatic analysis of equine ECG recordings. BMC Vet Res 2016;12(1):266.
    doi: 10.1186/s12917-016-0894-2google scholar: lookup
  58. Franklin SH, Van Erck‐Westergren E, Bayly WM. Respiratory responses to exercise in the horse. Equine Vet J 2012;44:726–732.
  59. Kozłowska N, Wierzbicka M, Jasiński T, Domino M. Advances in the diagnosis of equine respiratory diseases: a review of novel imaging and functional techniques. Animals 2022;12(3):381.
    doi: 10.3390/ani12030381google scholar: lookup
  60. Franklin SH, Burn JF, Allen KJ. Clinical trials using a telemetric endoscope for use during over‐ground exercise: a preliminary study. Equine Vet J 2008;40(7):712–715.
    doi: 10.2746/042516408x363783google scholar: lookup
  61. Bayly WM, Schultz DA, Hodgson DR, Gollnick PD. Ventilatory responses of the horse to exercise: effect of gas collection systems. J Appl Physiol 1987;63(3):1210–1217.
  62. Barnes GRG, Brennan M, Goulden BE, Kirkland J. Sound spectography in the diagnosis of equine respiratory disorders: a preliminary report. N Z Vet J 1979;27(7):145–146.
  63. Burn JF, Franklin SH. Measurement of abnormal respiratory sounds during overground exercise. Equine Vet J 2010;38(4):319–323.
  64. Derksen FJ, Holcombe SJ, Hartmann W, Robinson NE, Stick JA. Spectrum analysis of respiratory sounds in exercising horses with experimentally induced laryngeal hemiplegia or dorsal displacement of the soft palate. Am J Vet Res 2001;62(5):659–664.
    doi: 10.2460/ajvr.2001.62.659google scholar: lookup
  65. Franklin SH, Usmar SG, Lane JG, Shuttleworth J, Burn JF. Spectral analysis of respiratory noise in horses with upper airway disorders. Equine Vet J 2003;35(3):264–268.
  66. Jones S, Franklin S, Martin C, Steel C. Descriptive clinical studies complete upper airway collapse and apnoea during tethered swimming in horses. Equine Vet J 2020;52(3):352–358.
    doi: 10.1111/evj.13177google scholar: lookup
  67. Matsumoto T, Okumura S, Hirata S. Non‐contact respiratory measurement in a horse in standing position using millimeter‐wave array radar. J Vet Med Sci 2022;84(10):1340–1344.
    doi: 10.1292/jvms.22-0238google scholar: lookup
  68. De Fazio R, Stabile M, De Vittorio M, Velázquez R, Visconti P. An overview of wearable piezoresistive and inertial sensors for respiration rate monitoring. Electronics 2021;10(17):2178.
  69. Marlin DJ, Schrotert RC, Cashman PM, Deaton CM, Poole DC, Kindig CA. Movements of thoracic and abdominal compartments during ventilation at rest and during exercise. Equine Vet J 2002;34(S34):384–390.
  70. Green AR, Gates RS, Lawrence LM. Measurement of horse core body temperature. J Therm Biol 2005;30(5):370–377.
  71. Munsters C, Siegers E, Sloet Van Oldruitenborgh‐Oosterbaan M. Effect of a 14‐day period of heat acclimation on horses using heated indoor arenas in preparation for Tokyo Olympic games. Animals 2024;14(4):546.
    doi: 10.3390/ani14040546google scholar: lookup
  72. Ramey D, Bachmann K, Lee ML. A comparative study of non‐contact infrared and digital rectal thermometer measurements of body temperature in the horse. J Equine Vet Sci 2011;31(4):191–193.
  73. Verdegaal ELJMM, Howarth GS, McWhorter TJ, Delesalle CJG. Thermoregulation during field exercise in horses using skin temperature monitoring. Animals 2023;14(1):136.
    doi: 10.3390/ani14010136google scholar: lookup
  74. Kingston JK, Geor RJ. Use of dew‐point hygrometry, direct sweat collection, and measurement of body water losses to determine sweating rates in exercising horses. Am J Vet Res 1997;58(2):175–181.
  75. Matsui A, Osawa T, Fujikawa H, Asai Y, Matsui T, Yano H. Differences in unit area sweating rate among different areas of the body in exercising horses. J Equine Sci 2002;13(4):113–116.
    doi: 10.1294/jes.13.113google scholar: lookup
  76. Buchner HHF, Savelberg HHCM, 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.
  77. Serra Bragança FM, Rhodin M, Van Weeren PR. On the brink of daily clinical application of objective gait analysis: what evidence do we have so far from studies using an induced lameness model?. Vet J 2018;234:11–23.
  78. Crecan CM, Peștean CP. Inertial sensor technologies—their role in equine gait analysis, a review. Sensors 2023;23(14):6301.
    doi: 10.3390/s23146301google scholar: lookup
  79. Burger D, Vidondo B, Gerber V, Deillon D, Müller A, Scheidegger M. High‐level competition exercise and related fatigue are associated with stride and jumping characteristics in eventing horses. Equine Vet J 2024;56(3):631–641.
    doi: 10.1111/evj.13999google scholar: lookup
  80. 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.
    doi: 10.3390/ani14060921google scholar: lookup
  81. Serra Bragança FM, Roepstorff C, Rhodin M, Pfau T, Van Weeren PR, Roepstorff L. Quantitative lameness assessment in the horse based on upper body movement symmetry: the effect of different filtering techniques on the quantification of motion symmetry. Biomed Signal Process Control 2020;57:101674.
  82. Pfau T, Reilly P. How low can we go? Influence of sample rate on equine pelvic displacement calculated from inertial sensor data. Equine Vet J 2021;53(5):1075–1081.
    doi: 10.1111/evj.13371google scholar: lookup
  83. Valberg SJ. Muscle conditions affecting sport horses. Vet Clin North Am Equine Pract 2018;34(2):253–276.
  84. Kim JS, Hinchcliff KW, Yamaguchi M, Beard LA, Markert CD, Devor ST. Exercise training increases oxidative capacity and attenuates exercise‐induced ultrastructural damage in skeletal muscle of aged horses. J Appl Physiol 2005;98(1):334–342.
  85. Rivero JLL. Muscle biopsy as a tool for assessing muscular adaptation to training in horses. Am J Vet Res 1996;57(10):1412–1416.
  86. Ledwith A, McGowan CM. Muscle biopsy: a routine diagnostic procedure. Equine Vet Educ 2004;16(2):62–67.
  87. Chanda M, Srikuea R, Piyachaturawat P. Semi‐automated microbiopsy device: a potential tool for muscle sampling in horse. Thai J Vet Med 2016;46(4):569–577.
    doi: 10.56808/2985-1130.2776google scholar: lookup
  88. Stefaniuk M, Ropka‐Molik K, Piórkowska K, Bereta A, Szpar P, Czerwonka Z. Evaluation of minimally invasive muscle biopsy method for genetic analysis in horse. Ann Anim Sci 2015;15(3):621–627.
    doi: 10.1515/aoas-2015-0017google scholar: lookup
  89. Evans DL, Harris RC, Snow DH. Correlation of racing performance with blood lactate and heart rate after exercise in Thoroughbred horses. Equine Vet J 1993;25(5):441–445.
  90. Katz A, Sahlin K. Regulation of lactic acid production during exercise. J Appl Physiol 1988;65(2):509–518.
  91. Hauss AA, Stablein CK, Fisher AL, Greene HM, Nout‐Lomas YS. Validation of the lactate plus lactate meter in the horse and its use in a conditioning program. J Equine Vet Sci 2014;34(9):1064–1068.
  92. Siegers EW, Van Oldruifenborgh‐Oosterbaan MMS, Van Den Broek J, Munsters CCBM. Evaluation of three portable lactate‐measurement devices in exercising horses. Pferdeheilkunde 2018;34(2):141–144.
    doi: 10.21836/pem20180206google scholar: lookup
  93. Witkowska‐Piłaszewicz O, Ma'skoma'sko M, Domino M, Winnicka A. Infrared thermography correlates with lactate concentration in blood during race training in horses. Animals 2020;10:2072.
    doi: 10.3390/ani10112072google scholar: lookup
  94. Williams JM. Electromyography in the horse: a useful technology?. J Equine Vet Sci 2018;60:43–58.
  95. Timme S, Brand R. Affect and exertion during incremental physical exercise: examining changes using automated facial action analysis and experiential self‐report. PLoS One 2020;15(2):e0228739.
  96. Kim SM, Cho GJ. Analysis of various facial expressions of horses as a welfare indicator using deep learning. Vet Sci 2023;10(4):283.
    doi: 10.3390/vetsci10040283google scholar: lookup
  97. Valentin S, Zsoldos RR. Surface electromyography in animal biomechanics: a systematic review. J Electromyogr Kinesiol 2016;28:167–183.
  98. McKeever KH. The endocrine system and the challenge of exercise. Vet Clin North Am Equine Pract 2002;18(2):321–353.
  99. McKeever KH. Endocrine alterations in the equine athlete: an update. Vet Clin North Am Equine Pract 2011;27(1):197–218.
  100. Peluso MAM, Andrade LHSGD. Physical activity and mental health: the association between exercise and mood. Clinics 2005;60(1):61–70.
  101. Weinstein AA, Koehmstedt C, Kop WJ. Mental health consequences of exercise withdrawal: a systematic review. Gen Hosp Psychiatry 2017;49:11–18.
  102. Adams J, Kirkby R. Exercise dependence and overtraining: the physiological and psychological consequences of excessive exercise. Sports Med Train Rehabil 2001;10(3):199–222.
    doi: 10.1080/10578310210395google scholar: lookup
  103. Bartolomé E, Cockram MS. Potential effects of stress on the performance of sport horses. J Equine Vet Sci 2016;40:84–93.
  104. McGowan CM, Whitworth DJ. Overtraining syndrome in horses. Comp Exerc Physiol 2008;5(2):57.
    doi: 10.1017/s1478061508979202google scholar: lookup
  105. McBride SD, Mills DS. Psychological factors affecting equine performance. BMC Vet Res 2012;8(1):180.
    doi: 10.1186/1746-6148-8-180google scholar: lookup
  106. Hausberger M, Roche H, Henry S, Visser EK. A review of the human–horse relationship. Appl Anim Behav Sci 2008;109(1):1–24.
  107. Henderson AJZ. Don't fence me in: managing psychological well being for elite performance horses. J Appl Anim Welf Sci 2007;10(4):309–329.
    doi: 10.1080/10888700701555576google scholar: lookup
  108. Hall C, Randle H, Pearson G, Preshaw L, Waran N. Assessing equine emotional state. Appl Anim Behav Sci 2018;205:183–193.
  109. Gleerup KB, Forkman B, Lindegaard C, Andersen PH. An equine pain face. Vet Anaesth Analg 2015;42(1):103–114.
    doi: 10.1111/vaa.12212google scholar: lookup
  110. Van Loon JPAM, Van Dierendonck MC. Objective pain assessment in horses (2014–2018). Vet J 2018;242:1–7.
  111. Wathan J, Burrows AM, Waller BM, McComb K. EquiFACS: the equine facial action coding system. PLoS One 2015;10(8):e0131738.
  112. Torcivia C, McDonnell S. Equine discomfort ethogram. Animals 2021;11(2):580.
    doi: 10.3390/ani11020580google scholar: lookup
  113. Dyson S, Berger J, Ellis AD, Mullard J. Development of an ethogram for a pain scoring system in ridden horses and its application to determine the presence of musculoskeletal pain. J Vet Behav 2018;23:47–57.
  114. Mullard J, Berger JM, Ellis AD, Dyson S. Development of an ethogram to describe facial expressions in ridden horses (FEReq). J Vet Behav 2017;18:7–12.
  115. Ferlazzo A, Cravana C, Fazio E, Medica P. The different hormonal system during exercise stress coping in horses. Vet World 2020;13(5):847–859.
  116. Ladewig J, McLean AN, Wilkins CL, Fenner K, Christensen JW, McGreevy PD. A review of the ridden horse pain ethogram and its potential to improve ridden horse welfare. J Vet Behav 2022;54:54–61.
  117. Ask K, Rhodin M, Tamminen LM, Hernlund E, Haubro Andersen P. Identification of body behaviors and facial expressions associated with induced orthopedic pain in four equine pain scales. Animals 2020;10(11):2155.
    doi: 10.3390/ani10112155google scholar: lookup
  118. Anderson KA, Morrice‐West AV, Wong ASM, Walmsley EA, Fisher AD, Whitton RC. Poor association between facial expression and mild lameness in Thoroughbred trot‐up examinations. Animals 2023;13(11):1727.
    doi: 10.3390/ani13111727google scholar: lookup
  119. Miles KH, Clark B, Périard JD, Goecke R, Thompson KG. Facial feature tracking: a psychophysiological measure to assess exercise intensity?. J Sports Sci 2018;36(8):934–941.
  120. Feighelstein M, Riccie‐Bonot C, Hasan H, Weinberg H, Rettig T, Segal M. Automated recognition of emotional states of horses from facial expressions. PLoS One 2024;19(7):e0302893.
  121. Andersen PH, Broomé S, Rashid M, Lundblad J, Ask K, Li Z. Towards machine recognition of facial expressions of pain in horses. Animals 2021;11(6):1643.
    doi: 10.3390/ani11061643google scholar: lookup
  122. Munsters CCBM, Visser EK, Van Den Broek J, Sloet Van Oldruitenborgh‐Oosterbaan MM. Physiological and behavioral responses of horses during police training. Animal 2013;7(5):822–827.
    doi: 10.1017/s1751731112002327google scholar: lookup
  123. Norton T, Piette D, Exadaktylos V, Berckmans D. Automated real‐time stress monitoring of police horses using wearable technology. Appl Anim Behav Sci 2018;198:67–74.
  124. Munsters CCBM, Van Iwaarden A, Van Weeren R, Sloet Van Oldruitenborgh‐Oosterbaan MM. Exercise testing in Warmblood sport horses under field conditions. Vet J 2014;202(1):11–19.
  125. Rietmann TR, Stuart AEA, Bernasconi P, Stauffacher M, Auer JA, Weishaupt MA. Assessment of mental stress in Warmblood horses: heart rate variability in comparison to heart rate and selected behavioural parameters. Appl Anim Behav Sci 2004;88(1–2):121–136.
  126. Sikorska U, Maśko M, Ciesielska A, Zdrojkowski Ł, Domino M. Role of cortisol in horse's welfare and health. Agriculture 2023;13(12):2219.
  127. Peeters M, Sulon J, Beckers JF, Ledoux D, Vandenheede M. Comparison between blood serum and salivary cortisol concentrations in horses using an adrenocorticotropic hormone challenge: serum and salivary cortisol concentrations using an ACTH challenge. Equine Vet J 2011;43(4):487–493.
  128. Becker‐Birck M, Schmidt A, Lasarzik J, Aurich J, Möstl E, Aurich C. Cortisol release and heart rate variability in sport horses participating in equestrian competitions. J Vet Behav 2013;8(2):87–94.
  129. Strzelec K, Kankofer M, Pietrzak S. Cortisol concentration in the saliva of horses subjected to different kinds of exercise. Acta Vet Brno 2011;80(1):101–105.
    doi: 10.2754/avb201180010101google scholar: lookup
  130. Pawluski J, Jego P, Henry S, Bruchet A, Palme R, Coste C, et al. Low plasma cortisol and fecal cortisol metabolite measures as indicators of compromised welfare in domestic horses (Equus caballus). PLoS One. 2017;12(9):e0182257. https://doi.org/10.1371/journal.pone.0182257
  131. Niittynen T, Riihonen V, Moscovice LR, Koski SE. Acute changes in oxytocin predict behavioral responses to foundation training in horses. Appl Anim Behav Sci. 2022;254:105707. https://doi.org/10.1016/j.applanim.2022.105707
  132. Fenner K, Yoon S, White P, Starling M, McGreevy P. The effect of noseband tightening on horses' behavior, eye temperature, and cardiac responses. PLoS One. 2016;11(5):e0154179. https://doi.org/10.1371/journal.pone.0154179
  133. Bartolomé E, Sánchez MJ, Molina A, Schaefer AL, Cervantes I, Valera M. Using eye temperature and heart rate for stress assessment in young horses competing in jumping competitions and its possible influence on sport performance. Animal. 2013;7(12):2044–2053. https://doi.org/10.1017/S1751731113001626
  134. Valera M, Bartolomé E, Sánchez MJ, Molina A, Cook N, Schaefer A. Changes in eye temperature and stress assessment in horses during show jumping competitions. J Equine Vet Sci. 2012;32(12):827–830. https://doi.org/10.1016/j.jevs.2012.03.005
  135. Soroko M, Howell K, Zwyrzykowska A, Dudek K, Zielińska P, Kupczyński R. Maximum eye temperature in the assessment of training in racehorses: correlations with salivary cortisol concentration, rectal temperature, and heart rate. J Equine Vet Sci. 2016;45:39–45. https://doi.org/10.1016/j.jevs.2016.06.005
  136. Kim SM, Cho GJ. Validation of eye temperature assessed using infrared thermography as an indicator of welfare in horses. Appl Sci. 2021;11(16):7186. https://doi.org/10.3390/app11167186
  137. Mott RO, Hawthorne SJ, McBride SD. Blink rate as a measure of stress and attention in the domestic horse (Equus caballus). Sci Rep. 2020;10(1):21409. https://doi.org/10.1038/s41598-020-78386-z
  138. Merkies K, Ready C, Farkas L, Hodder A. Eye blink rates and eyelid twitches as a non‐invasive measure of stress in the domestic horse. Animals. 2019;9(8):562. https://doi.org/10.3390/ani9080562
  139. Ijichi C, Evans L, Woods H, Yarnell K. The right angle: validating a standardised protocol for the use of infra‐red thermography of eye temperature as a welfare indicator. Anim Welf. 2020;29(2):123–131. https://doi.org/10.7120/09627286.29.2.123
  140. Covalesky ME, Russoniello CR, Malinowski K. Effects of show‐jumping performance stress on plasma cortisol and lactate concentrations and heart rate and behavior in horses. J Equine Vet Sci. 1992;12(4):244–251. https://doi.org/10.1016/S0737-0806(06)81454-1
  141. Munk R, Jensen RB, Palme R, Munksgaard L, Christensen JW. An exploratory study of competition scores and salivary cortisol concentrations in Warmblood horses. Domest Anim Endocrinol. 2017;61:108–116. https://doi.org/10.1016/j.domaniend.2017.06.007
  142. Fureix C, Benhajali H, Henry S, Bruchet A, Prunier A, Ezzaouia M, et al. Plasma cortisol and faecal cortisol metabolites concentrations in stereotypic and non‐stereotypic horses: do stereotypic horses cope better with poor environmental conditions? BMC Vet Res. 2013;9(1):3. https://doi.org/10.1186/1746-6148-9-3
  143. Duran MC, Janz DM, Waldner CL, Campbell JR, Marques FJ. Hair cortisol concentration as a stress biomarker in horses: associations with body location and surgical castration. J Equine Vet Sci. 2017;55:27–33. https://doi.org/10.1016/j.jevs.2017.03.220
  144. Jolivald A, Ijichi C, Hall C, Yarnell K. The mane factor: compliance is associated with increased hair cortisol in the horse. Appl Anim Behav Sci 2023;258:105819. https://doi.org/10.1016/j.applanim.2022.105819
  145. Lansade L, Nowak R, Lainé AL, Leterrier C, Bonneau C, Parias C, et al. Facial expression and oxytocin as possible markers of positive emotions in horses. Sci Rep. 2018;8(1):14680. https://doi.org/10.1038/s41598-018-32993-z
  146. Lencioni GC, De Sousa RV, Souza Sardinha EJ, Corrêa RR, Zanella AJ. Pain assessment in horses using automatic facial expression recognition through deep learning‐based modeling. PLoS One. 2021;16(10):e0258672. https://doi.org/10.1371/journal.pone.0258672
  147. Miller M, Byfield R, Crosby M, Schiltz P, Johnson PJ, Lin J. A wearable photoplethysmography sensor for non‐invasive equine heart rate monitoring. Smart Agric Technol. 2023;5:100264. https://doi.org/10.1016/j.atech.2023.100264
  148. Chien MN, Fan SH, Huang CH, Wu CC, Huang JT. Continuous lactate monitoring system based on percutaneous microneedle array. Sensors. 2022;22(4):1468. https://doi.org/10.3390/s22041468
  149. Ming DK, Jangam S, Gowers SAN, Wilson R, Freeman DME, Boutelle MG, et al. Real‐time continuous measurement of lactate through a minimally invasive microneedle patch: a phase I clinical study. BMJ Innov. 2022;8(2):87–94. https://doi.org/10.1136/bmjinnov-2021-000864
  150. Vincent A, Peth‐Pierce RM, Morrissey MA, Acri MC, Guo F, Seibel L, et al. Evaluation of a modified bit device to obtain saliva samples from horses. Vet Sci. 2021;8(10):232. https://doi.org/10.3390/vetsci8100232
  151. Williams J, Gundry P, Richards J, Protheroe L. A preliminary evaluation of surface electromyography as a tool to measure muscle fatigue in the National Hunt racehorse. Vet Nurse. 2013;4(9):566–572. https://doi.org/10.12968/vetn.2013.4.9.566
  152. Smit IH, Mellbin Y, Ask K, te Moller NCR, Lundblad J. Quantifying facial expressions of the horse with optical motion capture and surface electromyography; a proof of concept. Wageningen: Noldus Information Technology; 2024. https://doi.org/10.6084/M9.FIGSHARE.25897855
  153. Mouloodi S, Rahmanpanah H, Gohery S, Burvill C, Tse KM, Davies HMS. What can artificial intelligence and machine learning tell us? A review of applications to equine biomechanical research. J Mech Behav Biomed Mater. 2021;123:104728. https://doi.org/10.1016/j.jmbbm.2021.104728

Citations

This article has been cited 0 times.