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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM30132
Volume:
8 |
4 |
Apr 2024
Pages:
Publication Frequency:
Monthly (12 issues per year)
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.
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
Publication Date
12/31/45385
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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No funding information available for this research.
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