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YoNet: A Neural Network for Yoga Pose Classification

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posted on 2023-02-20, 09:55 authored by Faisal Bin Ashraf, Muhammad Usama Islam, Md Rayhan Kabir, Jasim Uddin

 Yoga has become an integral part of human life to maintain a healthy body and mind in recent times. With the growing, fast-paced life and work from home, it has become difficult for people to invest time in the gymnasium for exercises. Instead, they like to do assisted exercises at home where pose recognition techniques play the most vital role. Recognition of different poses is challenging due to proper dataset and classification architecture. In this work, we have proposed a deep learning-based model to identify five different yoga poses from comparatively fewer amounts of data. We have compared our model’s performance with some state-of-the-art image classification models-ResNet, InceptionNet, InceptionResNet, Xception and found our architecture superior. Our proposed architecture extracts spatial, and depth features from the image individually and considers them for further calculation in classification. The experimental results show that it achieved 94.91% accuracy with 95.61% precision. 

History

Published in

SN Computer Science

Publisher

Springer

Version

  • VoR (Version of Record)

Citation

Ashraf, F. B., Islam, M. U., Kabir, M. R., & Uddin, J. (2023). 'YoNet: A Neural Network for Yoga Pose Classification', SN Computer Science, 4(2), 198.

Electronic ISSN

2661-8907

Cardiff Met Affiliation

  • Cardiff School of Technologies

Cardiff Met Authors

Jasim Uddin

Copyright Holder

  • © The Authors

Language

  • en

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