Enhancement and Evaluation of Workout Postures through AI-Powered Pose Estimation

Strength training and yoga carry injury risks for beginners, demanding tailored feedback. Advances in deep learning present real-world applications, yet its adoption in fitness remains limited. This study explores human pose estimation for workout feedback, emphasizing technique-related issues. An analytical approach involves developing a system for exercise and filming detection and evaluating technique aspects.3 The research aims to establish a foundation for an exercise feedback application through a comparative study of two deep learning models. Testing focuses on ten exercise categories. A comprehensive review of pose estimation systems evaluates their effectiveness, demonstrating promise for workout analysis. Despite limitations, further exploration is warranted for this promising application. Keywords- Visual Perception of Computers, Advanced Convolutional Neural Networks, Body Position Assessment, Exercise Identification.