Stride Ahead: Preventing Injuries in High-Performance Horses Through Predictive Gait Analysis
Background:
In competitive equestrian sports, minor irregularities in stride and posture often precede serious injuries. However, these early signs are often missed due to reliance on observational techniques alone.
The IZZ Approach:
IZZ coordinated a case study involving equine biomechanics researchers, AI imaging specialists, and conditioning trainers to proactively address injury risks through advanced gait and fatigue modeling.
Proposed Solution:
- Weekly 3D motion capture sessions to analyze gait asymmetries.
- Machine learning model trained to detect deviations from optimal stride dynamics.
- Recovery and training schedules adjusted based on AI-generated insights.
- Supplements like Fisetin and NAD+ introduced to support muscular regeneration.
Projected Impact:
- 25% reduction in injuries over a 3-month competition period.
- Improved muscle elasticity and joint alignment.
- Early detection of stress markers before they develop into clinical issues.
- Better long-term post-career health for horses.
Supported By:
Research from FEI Campus Biomechanics Course, Equine Veterinary Journal, 2020, and AI gait analysis studies.