ArticleScientific reports2020
Improving gait classification in horses by using inertial measurement unit (IMU) generated data and machine learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 64 citations in OpenAlex.
- The effect of full-limb flexion tests on static and dynamic muscle activity and locomotion asymmetry in owner-sound horses.Equine veterinary journal · 2026Article
- Validation of a handheld smartphone markerless gait-analysis tool using an estimated groundline in horses.Equine veterinary journal · 2026Article
- Machine learning-based gait classification and genome-wide association identify a QTL for gait type in Colombian paso horses.iScience · 2026Article
- The influence, promise, and potential perils of artificial intelligence in veterinary medicine: a call for improved awareness and literacy.Journal of veterinary internal medicine · 2026Article
- Adaptation strategies of Icelandic horses with induced transient hindlimb lameness at walk, trot and tölt.Equine veterinary journal · 2026Article
- Player-Reported Perceptions of Lameness Risks and Contributing Factors for Polo Horses: Results from a Survey.Animals : an open access journal from MDPI · 2025Article
- Bayesian Recursive and Structural Equation Models to Infer Causal Links Among Gait Visual Scores on Campolina Horses.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2025Article
- Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review.Veterinary medicine and science · 2025Review
- Discrimination of the Lame Limb in Horses Using a Machine Learning Method (Support Vector Machine) Based on Asymmetry Indices Measured by the EQUISYM System.Sensors (Basel, Switzerland) · 2025Article
- Variation in Four Horse Gait Categories Illustrated by Quantitative Analysis With ANALOC-E.Veterinary medicine international · 2025Article
- Applying Multi-Purpose Commercial Inertial Sensors for Monitoring Equine Locomotion in Equestrian Training.Sensors (Basel, Switzerland) · 2024Article
- Supervised Machine Learning Techniques for Breeding Value Prediction in Horses: An Example Using Gait Visual Scores.Animals : an open access journal from MDPI · 2024Article
- Inertial sensor data of horses from four breeds at walk and trot in hand on a straight line.Data in brief · 2024Article
- Objective Assessment of Equine Locomotor Symmetry Using an Inertial Sensor System and Artificial Intelligence: A Comparative Study.Animals : an open access journal from MDPI · 2024Article
- Inertial Sensor Technologies-Their Role in Equine Gait Analysis, a Review.Sensors (Basel, Switzerland) · 2023Review
- Prediction of continuous and discrete kinetic parameters in horses from inertial measurement units data using recurrent artificial neural networks.Scientific reports · 2023Article
- Efficient Development of Gait Classification Models for Five-Gaited Horses Based on Mobile Phone Sensors.Animals : an open access journal from MDPI · 2023Article
- Timing of Vertical Head, Withers and Pelvis Movements Relative to the Footfalls in Different Equine Gaits and Breeds.Animals : an open access journal from MDPI · 2022Article
- The Effect of Chiropractic Treatment on Limb Lameness and Concurrent Axial Skeleton Pain and Dysfunction in Horses.Animals : an open access journal from MDPI · 2022Article
- Detection of Horse Locomotion Modifications Due to Training with Inertial Measurement Units: A Proof-of-Concept.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
15 authors at 9 institutions in 6 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For centuries humans have been fascinated by the natural beauty of horses in motion and their different gaits. Gait classification (GC) is commonly performed through visual assessment and reliable, automated methods for real-time objective GC in horses are warranted. In this study, we used a full body network of wireless, high sampling-rate sensors combined with machine learning to fully automatically classify gait. Using data from 120 horses of four different domestic breeds, equipped with seven motion sensors, we included 7576 strides from eight different gaits. GC was trained using several machine-learning approaches, both from feature-extracted data and from raw sensor data. Our best GC model achieved 97% accuracy. Our technique facilitated accurate, GC that enables in-depth biomechanical studies and allows for highly accurate phenotyping of gait for genetic research and breeding. Our approach lends itself for potential use in other quadrupedal species without the need for developing gait/animal specific algorithms.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.