Abstract
In today's fitness landscape, many individuals face challenges in developing workout plans that are tailored to their unique fitness levels, body types, and available equipment. Generic workout routines often fail to meet the specific needs of users, leading to suboptimal results and decreased motivation over time. This paper proposes developing a web-based system that utilizes reinforcement learning (RL) to deliver personalized exercise recommendations. By continuously learning from user feedback and progress, the system adapts workout plans dynamically to maximize effectiveness and sustainability. The RL model considers factors such as user fitness levels, personal preferences, body type, and available equipment to suggest optimal exercises and routines. This approach fosters an individualized fitness experience, improving adherence, results, and long- term user engagement. The proposed system could revolutionize how individuals approach fitness by offering customized solutions that evolve as the user progresses on their fitness journey.
Published Version
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