Diagnosing stress and exhaustion in early-stage performance horses is essential for training better, preventing injuries, and ensuring the well-being of the entire equine. WSNs bring together multiple physiological and environmental sensors and provide a non-invasive solution for real-time monitoring. Such networks can continuously measure critical variables such as heart rate, respiration rate, body temperature, movement patterns, and ambient conditions, and wirelessly transmit this information to centralized computers for analysis. High-level algorithms and machine learning models can then be used to detect subtle abnormalities that may indicate stress, fatigue, or imminent overstraining. Such a technology will enable a trainer/veterinarian to intervene promptly, adjust training programs, and maximize recovery, thereby enhancing performance with minimal health risks. WSNs offer several advantages over conventional monitoring approaches, including mobility, scalability, and continuous high-resolution data collection. Monitoring equine performance using WSNs is an essential step towards precision management and evidence-based decision-making. It helps fill the gap between equine physiology and data-driven performance optimization. This paper focuses on the design, development, and potential uses of WSNs for detecting early warning signs of stress and fatigue in performance horses, and on their significance for the development of equine sports science and equine welfare.
Keywords: Wireless Sensor Network (WSN); Early Detection (ED); Stress (SS); Fatigue (FF); Performance Horses (PH)
Keywords: horses, chronic recurrent cecal impaction, surgical treatment enlargement cecocolic orifice, long-term survival
