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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM30132

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

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM30132

Volume:

8 |

4 |

Apr 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM30132

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

AB

Aniket Bodhe

NM

Nikhil Muneshwar

HM

Harshali Mohurle

SK

Sakshi Kusram

SS

Shailendra Shende

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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.

Cite This Article

Aniket Bodhe, Nikhil Muneshwar, Harshali Mohurle, Sakshi Kusram, Shailendra Shende, "Enhancement and Evaluation of Workout Postures through AI-Powered Pose Estimation," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 4, Apr 2024. https://doi.org/10.55041/IJSREM30132

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12/31/45385

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