A pattern recognition approach for the quantification of horse and rider interactions
Authors: SCHÖLLHORN W. I., PEHAM C., LICKA T., SCHEIDL M.
Journal: Equine Veterinary Journal
Summary
# Editorial Summary Quantifying the dynamic interaction between horse and rider remains largely unexplored in equine biomechanics, despite the clear importance of rider skill and adaptation in determining performance and welfare outcomes. Schöllhorn and colleagues employed three-dimensional high-speed video analysis (120 Hz) to capture movement patterns in 14 horses trotting both free and ridden by professional and recreational riders, extracting angular data from key joints (fetlock, hock, stifle, hip, carpus, elbow, shoulder, back, and head) and analysing these trajectories using artificial neural networks and cluster analysis. Head movement emerged as predominantly rider-controlled, whilst hind fetlock and hock motion remained largely unaffected by the rider's presence—suggesting these regions maintain inherent locomotor integrity regardless of ridden status. Critically, the professional rider demonstrated significantly greater synchronisation with the horse's movement patterns compared to the recreational rider, providing objective evidence that skilled horsemanship involves meaningful neuromuscular adaptation rather than simply imposing movement. This pattern-recognition methodology offers a quantifiable framework for assessing rider-horse biomechanical compatibility and rider competence, with applications for training evaluation, coaching feedback, and investigating how individual horse morphology and temperament should inform rider matching and technique refinement.
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Practical Takeaways
- •Professional riders achieve better results through active adaptation to their horse's natural movement pattern rather than forcing a predetermined style—focus on feel and responsiveness to your individual horse
- •Rider influence is most pronounced at the head and neck; excessive head manipulation may indicate a mismatch between rider and horse rather than superior control
- •Movement analysis tools using video and artificial intelligence can objectively assess rider-horse compatibility and identify which horses suit different rider skill levels
Key Findings
- •Head angle movement patterns are primarily dominated by rider influence, showing clear differences between ridden and hand-led conditions
- •Hind fetlock and hock movement patterns remain largely unchanged between ridden and hand-led conditions, indicating minimal rider effect on these variables
- •Professional riders demonstrate significantly higher adaptation to individual horse movement patterns compared to recreational riders
- •Artificial neural networks (Kohonen maps) can effectively quantify and differentiate rider-specific versus horse-specific movement patterns during trotting