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Equine veterinary journal2022; doi: 10.1111/evj.13909

Validation of inertial measurement units to detect and predict horse behaviour while stabled.

Abstract: Musculoskeletal injuries are observed in Thoroughbred racehorses and may become catastrophic. Currently, there are limited methods for early detection of such injuries. Most injuries develop gradually due to accumulated damage, providing the opportunity for early detection. Horses experiencing pain or lameness may exhibit changes in behaviour so the development of an objective, real-time system monitoring horse behaviour may enable detection of bone injuries before catastrophic failure. Objective: To determine whether intensive observational methods of assessing horse behaviour can be replaced by use of inertial measurement units (IMUs). Methods: Validation study assessing IMU use against video observation. Methods: Six hospitalised Thoroughbreds (algorithm training data) and 19 Thoroughbred racehorses in-training (algorithm testing data) were equipped with an IMU placed on the lateral side of both forelimbs (left fore, LF; right fore, RF) and monitored in a stable for 4 h. An algorithm was developed to classify behaviour and then validated against video recordings. Results: Standing was the most prevalent behaviour (LF 88.8%, 95% confidence interval [CI] 88.7-89.0; RF 88.5%, 95% CI 88.4-88.7). IMU classification of recumbent and standing activities showed excellent agreement (sensitivity) with video observation (>98%). This was followed by stepping (LF 89.4%, RF 85.5%) then weight-shifting (LF 54.3%, RF 61.5%). Predictions from the algorithm showed misclassification of 2.5% (LF 5500/225 352, RF 5218/210 170). Excluding standing, misclassification was 6.8% (1705/25 158) and 7.5% (1812/24 077) for the left and right forelimbs, respectively, with pawing and weight-shifting most frequently misclassified. Conclusions: Increasing the number of horses and types of behaviours observed may improve predictions. Conclusions: IMUs displayed a high sensitivity to movement on a small number of horses, and with further validation they have the potential to effectively monitor behaviour of racehorses in training. However, more sensitive methods may be needed to validate the classification of weight-shifting behaviour. Future studies should evaluate the association between each behaviour and musculoskeletal injury. Unassigned: Muskuloskeletale Verletzungen werden bei Vollblutrennpferden beobachtet und können katastrophale Folgen haben. Derzeit gibt es nur wenige Methoden zur Früherkennung solcher Verletzungen. Die meisten Verletzungen entwickeln sich allmählich aufgrund von kumulierten Schäden, was die Möglichkeit einer frühzeitigen Erkennung bietet. Pferde, die unter Schmerzen oder Lahmheit leiden, zeigen oft Verhaltensänderungen, so dass die Entwicklung eines objektiven Echtzeitsystems zur Überwachung des Pferdeverhaltens die Erkennung von Knochenverletzungen vor einem katastrophalen Ausfall ermöglichen könnte. Unassigned: Es soll ermittelt werden, ob intensive Beobachtungsmethoden zur Beurteilung des Pferdeverhaltens durch den Einsatz von Inertialen Messeinheiten (IMUs) ersetzt werden können. Methods: Validierungsstudie zur Bewertung der IMU-Nutzung im Vergleich zur Videobeobachtung. Methods: Sechs stationär behandelte Vollblüter (Algorithmus-Trainingsdaten) und 19 Vollblut-Rennpferde im Training (Algorithmus-Testdaten) wurden mit einer IMU, die an der lateralen Seite beider Vordergliedmaßen (vorne links, VL; vorne rechts, VR) angebracht wurde, ausgestattet und vier Stunden lang in einem Stall überwacht. Es wurde ein Algorithmus zur Klassifizierung des Verhaltens entwickelt und anschließend anhand von Videoaufnahmen validiert. Unassigned: Das Stehen war das häufigste Verhalten (VL 88.8%, 95% CI 88.7, 89.0; VR 88.5%, 95% CI 88.4, 88.7). Die IMU-Klassifizierung von Aktivitäten im Liegen und Stehen zeigte eine ausgezeichnete Übereinstimmung (Sensitivität) mit der Videobeobachtung (>98%). Es folgte das Gehen (VL 89.4%, VR 85.5%) und dann die Gewichtsverlagerung (VL 54.3%, VR 61.5%). Die Vorhersagen des Algorithmus zeigten eine Fehlklassifizierung von 2.5% (VL 5500/225 352, VR 5218/210 170). Ohne das Stehen lag die Fehlklassifizierung bei 6.8% (1705/25 158) bzw. 7.5% (1812/24 077) für die linke bzw. rechte Vordergliedmaße, wobei Scharren und Gewichtsverlagerung am häufigsten falsch klassifiziert wurden. Unassigned: Eine Erhöhung der Anzahl der Pferde und der beobachteten Verhaltensweisen kann die Vorhersagen verbessern. Unassigned: Die IMUs zeigten bei einer kleinen Anzahl von Pferden eine hohe Sensibilität für die Bewegungen, und bei weiterer Validierung haben sie das Potenzial, das Verhalten von Rennpferden im Training effektiv zu überwachen. Es könnten jedoch empfindlichere Methoden erforderlich sein, um die Klassifizierung des Gewichtsverlagerungsverhaltens zu validieren. In künftigen Studien sollte der Zusammenhang zwischen den einzelnen Verhaltensweisen und Verletzungen des Bewegungsapparats untersucht werden.
© 2022 The Authors. Equine Veterinary Journal published by John Wiley & Sons Ltd on behalf of EVJ Ltd.
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Publication Date: 2022-12-20 PubMed ID: 36537838DOI: 10.1111/evj.13909Google 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 research study is focused on examining the efficacy of inertial measurement units (IMUs) in predicting and detecting behaviour changes in horses which are indicative of musculoskeletal injuries. The findings suggest that while IMUs show promise in accurately classifying certain horse behaviours, further research and improvement in techniques may be necessary for better results.

Objective and Methods

  • The study was conducted to determine if extensive observational methods for assessing horse behaviour could be replaced with IMUs. These devices have been proposed as a non-invasive method to monitor horse behaviour in real-time, thus aiding in the early detection of musculoskeletal injuries.
  • The researchers used a validation study method, which evaluated the use of IMUs against standard video observation.
  • A total of 25 Thoroughbred racehorses participated in the study – six were hospitalised (used for algorithm training data), and 19 were in-training (used for algorithm testing data).
  • Each horse had an IMU attached to the lateral side of both forelimbs for a four-hour monitoring session in a stable.
  • An algorithm was developed to categorise horse behaviour, which was then validated against video recordings.

Results

  • Standing behaviour was the most prevalent, as recorded by the IMUs.
  • The IMUs were highly accurate (>98% sensitivity) in classifying standing and recumbent activities, as confirmed by video observation.
  • Other behaviours, such as stepping and weight-shifting, were less accurately categorised, with sensitivity rates of 89.4% and 54.3% for the left forelimb, and 85.5% and 61.5% for the right forelimb, respectively.
  • Predictions made by the algorithm showed a misclassification rate of 2.5%. However, if standing behaviour was excluded, the misclassification rate increased to 6.8% and 7.5% for the left and right forelimbs, respectively. The behaviours most frequently misclassified were pawing and weight-shifting.

Conclusions and Future Directions

  • Although the IMUs showed high sensitivity to horse movement, and thus potential for monitoring behaviour in training racehorses, the study identified room for improvement. More sensitive methods may be required to validate the classification of weight-shifting behaviour.
  • The researchers suggest an increase in the sample size and the variety of observed behaviours could potentially improve the prediction rates.
  • Future studies are needed to explore the relationship between different behaviours and musculoskeletal injuries for a more effective application of IMUs in detecting and predicting horse injuries.

Cite This Article

APA
Anderson K, Morrice-West AV, Walmsley EA, Fisher AD, Whitton RC, Hitchens PL. (2022). Validation of inertial measurement units to detect and predict horse behaviour while stabled. Equine Vet J. https://doi.org/10.1111/evj.13909

Publication

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

Researcher Affiliations

Anderson, Katrina
  • Equine Lameness and Imaging Centre, Melbourne Veterinary School, University of Melbourne, Werribee, Victoria, Australia.
Morrice-West, Ashleigh V
  • Equine Lameness and Imaging Centre, Melbourne Veterinary School, University of Melbourne, Werribee, Victoria, Australia.
Walmsley, Elizabeth A
  • Equine Lameness and Imaging Centre, Melbourne Veterinary School, University of Melbourne, Werribee, Victoria, Australia.
Fisher, Andrew D
  • Animal Welfare Science Centre, Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Parkville, Victoria, Australia.
Whitton, R Chris
  • Equine Lameness and Imaging Centre, Melbourne Veterinary School, University of Melbourne, Werribee, Victoria, Australia.
Hitchens, Peta L
  • Equine Lameness and Imaging Centre, Melbourne Veterinary School, University of Melbourne, Werribee, Victoria, Australia.

Grant Funding

  • Racing Victoria
  • University of Melbourne
  • Victorian Racing Industry Fund of the Victoria State Government

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Citations

This article has been cited 1 times.
  1. Nowak M, Martin-Cirera A, Jenner F, Auer U. Time budgets and weight shifting as indicators of pain in hospitalized horses. Front Pain Res (Lausanne) 2024;5:1410302.
    doi: 10.3389/fpain.2024.1410302pubmed: 39109240google scholar: lookup
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