Mihir Chauhan,
Simran Jadhav,
Rohan Jain,
Devesh Karde,
- Student Department of Computer Engineering, Rajiv Gandhi Institute of Technology, Mumbai Maharashtra India
- Student Department of Computer Engineering, Rajiv Gandhi Institute of Technology, Mumbai Maharashtra India
- Student Department of Computer Engineering, Rajiv Gandhi Institute of Technology, Mumbai Maharashtra India
- Student Department of Computer Engineering, Rajiv Gandhi Institute of Technology, Mumbai Maharashtra India
Abstract
Our project introduces a pioneering method for enhancing yoga practice through real-time pose correction, utilizing cutting-edge computer vision technology. Originating in India 5000 years ago, yoga offers profound benefits for both body and mind, yet with the modern lifestyle’s increasing stress levels, its popularity has surged globally. While various avenues exist for learning yoga, including classes at yoga centers and self-learning through books and videos, many individuals struggle to identify and rectify inaccuracies in their poses. Our solution addresses this challenge by allowing users to select a pose they wish to practice and use a webcam for analysis. Our technology uses computer vision algorithms to analyze the angles of different body joints and compares the user’s pose with an expert’s reference pose. Based on this comparison, users can receive personalized feedback to improve their posture. Bridging traditional yoga instruction with modern technology, our project aims to deepen users’ yoga practice, fostering greater awareness and alignment while enhancing effectiveness and safety.
Keywords: MoveNet thunder, OpenCV, neural network, yoga
[This article belongs to Journal of Operating Systems Development & Trends(joosdt)]
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Journal of Operating Systems Development & Trends
| Volume | 11 |
| Issue | 02 |
| Received | June 28, 2024 |
| Accepted | July 31, 2024 |
| Published | August 8, 2024 |