Automated horse gait classification
Automated horse gait classification is an innovative field that focuses on distinguishing and recognising the various gaits or ways a horse moves, a task that has historically been mastered by seasoned equestrians. The foundation of this research traces back to the pioneering work of Milton Hildebrand in the 1960s, who initiated scientific interest in the patterns and complexities of horse locomotion.[1]
Historically, a considerable amount of research concentrated on the use of equine-mounted sensors, tools affixed directly onto the horses to gather data about their gaits. These devices have proven to be highly efficient, achieving a classification accuracy of over 97%.[2] These high-accuracy sensors are instrumental in identifying and differentiating between gaits, providing a detailed snapshot of equine movement patterns.
In a shift from the traditional approach, recent studies have embraced the advent of rider-mounted sensors. Even in these early stages of implementation, smartphone sensors in the rider's pocket exhibit a robust classification accuracy rate exceeding 93%.[3]
References
- ↑ Hildebrand, Milton (1965-11-05). "Symmetrical Gaits of Horses: Gaits can be expressed numerically and analyzed graphically to reveal their nature and relationships". Science. 150 (3697): 701–708. doi:10.1126/science.150.3697.701. ISSN 0036-8075. PMID 5844074.
- ↑ 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.; Gmel, A. I.; Roepstorff, C.; van der Zwaag, B. J.; Van Weeren, P. R.; Hernlund, E. (2020-10-20). "Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning". Scientific Reports. 10 (1): 17785. Bibcode:2020NatSR..1017785S. doi:10.1038/s41598-020-73215-9. ISSN 2045-2322. PMC 7576586 Check
|pmc=value (help). PMID 33082367 Check|pmid=value (help). - ↑ Davíðsson, Haraldur B.; Rees, Torben; Ólafsdóttir, Marta Rut; Einarsson, Hafsteinn (January 2023). "Efficient Development of Gait Classification Models for Five-Gaited Horses Based on Mobile Phone Sensors". Animals. 13 (1): 183. doi:10.3390/ani13010183. ISSN 2076-2615. PMC 9817528 Check
|pmc=value (help). PMID 36611791 Check|pmid=value (help).
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