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






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

Aniket Bodhe1, Nikhil Muneshwar2, Harshali Mohurle3, Sakshi Kusram4, Shailendra Shende5

1, 2, 3, 4, 5 Department of Computer Science and Engineering, Government College of Engineering Chandrapur, Maharashtra, India

Abstract: 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.

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