Horse riding falls under the “Sport for Life” disciplines, where a long-term equestrian development can provide a clear pathway of developmental stages to help individuals, inclusive of those with a disability, to pursue their goals in sport and physical activity, providing long-term health benefits. However, the biomechanical interaction between horse and (disabled) rider is not wholly understood, leaving challenges and opportunities for the horse riding sport. On the basis of analysing previously collected data, machine learning algorithms will be selected for implementation in existing or modified EquiMoves sensor hardware and software solutions.
Target applications and follow-ups include:
- Improving horse and (disabled) rider interaction for riders of all skill levels;
- Objective evidence-based classification system for competitive grading of disabled riders in Para Dressage events;
- Identifying biomechanical irregularities for detecting and/or preventing injuries of horses.
Topic
Sensor hardware, data analysis, AI/machine learning, horse and rider biomechanics, veterinary medicine
Program objectives
The purpose of this KIEM project is to start an interdisciplinary collaboration between parties interested in integrating existing knowledge on horse and (disabled) rider interaction with any novel insights to be gained from analysing recently collected sensor data using the EquiMoves™ system.
Partners
Saxion University of Applied Sciences (Lead manager), Rosmark Consultancy, Inertia Technology, Utrecht University, Sport Horse Health Plan, University of Central Lancashire, Swedish and University of Agricultural.Sciences
Duration
1 March 2021 until 1 March 2022
More information
Funding
This project is funded by SIA.