AI-Enabled Motion Analysis and Personalized Training Programs to Improve Physical Fitness Performance in Higher Education Students

Abstract

Emerging advancements in Artificial Intelligence (AI) have introduced innovative applications in sports science and Physical Education (PE). Traditional fitness assessment methods rely largely on human observation, resulting in subjective evaluations and limited performance tracking. AI-enabled technologies provide automated motion analysis, biomechanical feedback, and personalized performance enhancements in real time. This research proposes a structured investigation into the effectiveness of AI-driven personalized training programs on fitness levels among higher education students. The study utilizes machine learning-based pose estimation systems, wearable IoT devices, and adaptive fitness dashboards for continuous monitoring of agility, balance, endurance, and postural accuracy. The expected outcome suggests significant improvements in athletic skills and motivation compared to conventional routine-based physical training. This research emphasizes the future role of AI in transforming PE departments into data-enabled, performance-oriented learning ecosystems.