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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM35578

AI-Assisted yoga Asanas in the future using Deep Learning and Posenet

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM35578

Volume:

8 |

6 |

Jun 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM35578

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

KD

Krupali Dhawale

MR

Minakshi Ramteke

PD

Pranali Dhawas

MS

Madhuri Sahu

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Abstract

Yoga instructors have become teaching online due to the ongoing pandemic. Despite studying from top sources including videos, blogs, journals, and essays, users need live tracking to ensure proper posture and health. While technology can aid, beginner-level yoga practitioners rely on their teacher for identifying their proper posture. The main objective of yoga pose detection and corrections is to deliver standard and precise poses for yoga using computer vision. If the yoga pose is not performed correctly, it may lead to severe injuries and long-term problems. Analysing human positions to identify and fix yoga positions can help humans live better lives in their own homes. Our work focuses on experimenting with different approaches to yoga position classification, therefore using PoseNet . Using such algorithms of deep learning, a person can determine the correct/ideal way/method to perform the particular yoga asana he or she is attempting to perform.

KEYWORDS

Deep Learning Posenet Pose Estimation posture recognition tensor flow lite

Cite This Article

Krupali Dhawale, Minakshi Ramteke, Pranali Dhawas, Madhuri Sahu, "AI-Assisted yoga Asanas in the future using Deep Learning and Posenet," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 6, Jun 2024. https://doi.org/10.55041/IJSREM35578

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Publication Date

6/5/2024

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Double-Blind Peer Review

Review Rounds: 2

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

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