Generating Horse Training Session Feedback
Project description
The Equine Feedback System is a rule-based application that analyzes horse training data and provides feedback based on predefined conditions. By monitoring metrics such as heart rate, workload, and recovery, the system helps trainers identify potential signs of fatigue or overtraining and supports informed training decisions.
Context
Horses play a central role in the equestrian industry, where their health, wellbeing, and performance are essential for training and competition. Because horses cannot verbally communicate discomfort, it can be difficult for trainers to detect early signs of fatigue, stress, or overtraining based on observation alone.
The Equine Feedback System uses a rule-based approach to analyze horse-related training data such as heart rate, workload, and recovery. Based on predefined conditions, the system provides feedback to help identify potential risks and support better-informed training decisions.
The goal is to improve horse welfare by ensuring training intensity is balanced with adequate recovery, helping to maintain long-term health and consistent performance.
Results
By pivoting from complex AI to transparent if-else logic and loops, our system successfully answers the client's core question: "Did I train my horse correctly?". The system adjusts baseline expectations for heat (over 20–25°C), categorizes performance into clear physiological states (Normal, Fitness Improvement, or Fatigue), checks for poor heart rate recovery (~70 bpm), and filters the Acute:Chronic Workload Ratio (ACWR) through metadata like age, level, and discipline.
The final result eliminates abstract data and "black box" confusion, delivering concrete, actionable feedback that trainers can trust, such as confirming an excellent, sustaining session while explicitly warning that adding a third consecutive hard day will cause dangerous overtraining.