Hybrid AI and Optimization Algorithm for Personalized Workout Planning

With the increasing demand for personalized fitness solutions, traditional workout plans often lack adaptability and efficiency. This paper introduces a hybrid AI and optimization-based algorithm that personalizes workout routines dynamically, improving adherence, efficiency, and safety. The system integrates reinforcement learning for real-time adaptation, machine learning for injury risk assessment, and integer programming for optimized scheduling. A real-time feedback mechanism ensures each workout aligns with the user’s progress, fatigue, and constraints. Experimental results indicate an average improvement of 16.95% in adherence, a 44.70% gain in workout efficiency, and a 30.90% reduction in injury risk compared to conventional approaches. These findings demonstrate the potential of AI-driven fitness planning in enhancing user experience and overall fitness outcomes.