We’re pleased to announce a new technological update in the HorseCare project.

HorseCare has introduced a more advanced machine-learning–based motion tracking system that allows the reconstruction of horse movement trajectories even in areas where GPS is unavailable. This includes indoor arenas, covered riding halls, and locations with unstable satellite signal.

What has changed

Unlike traditional tracking methods that rely heavily on GPS data, the new system analyzes sensor-based motion patterns. This approach enables consistent trajectory visualization regardless of external positioning limitations.

At the current stage:

• The average motion tracking accuracy reaches around 92%

• Movement visualization is now available in GPS-limited environments

• Training sessions indoors and under cover can be analyzed more completely

Continuous development

This technology is still evolving. While it does not yet represent the final stage of development, the current level of accuracy already provides meaningful insights into movement dynamics. The system will continue to improve as additional data is collected and the ML models are further refined.

Why it matters

The new ML tracking system expands the analytical capabilities of HorseCare and reduces dependence on GPS, allowing horse owners and professionals to:

• Monitor movement consistency in any training environment

• Maintain data continuity between indoor and outdoor sessions

• Gain deeper insight into workload and motion patterns

We remain committed to developing HorseCare as a reliable tool for understanding equine movement and supporting informed training and health-related decisions.