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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM18078

Realtime Gym AI

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM18078

Volume:

7 |

3 |

Mar 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

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

BB

Bhushan Bari

SK

Shital Khairnar

AS

Anand Suryawanshi

AT

Ankit Tamboli

PSMK

Prof. Sunil M. Kale

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Abstract

The adoption of a healthy lifestyle is a challenge for many people, and joining a gym is often one of the first steps. However, many individuals struggle with the cost, accessibility, and lack of guidance on proper technique. This problem is addressed through an automated posture detection model, which uses machine learning to recognize different human postures in real-time and help correct them.The posture detection model involves a computer vision system that utilizes machine learning algorithms to analyze live video feeds of individuals exercising in the gym. The model can detect and track body postures in real-time, providing immediate feedback on any mistakes in form or technique. This feedback can be in the form of visual cues or audio prompts, allowing users to adjust their posture and prevent injuries.By providing immediate and personalized feedback, the posture detection model addresses the main barriers that people face when joining a gym. It eliminates the need for costly personal trainers, reduces the risk of injury, and saves time by providing on-the-spot corrections. Overall, this model has the potential to revolutionize the fitness industry, making healthy living more accessible and attainable for everyone.

KEYWORDS

Posture detection Machine Learning Deep Learning

Cite This Article

Bhushan Bari, Shital Khairnar, Anand Suryawanshi, Ankit Tamboli, Prof. Sunil M. Kale, "Realtime Gym AI," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 3, Mar 2023.

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Publication Date

5/23/2023

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Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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