YoNet: A Neural Network for Yoga Pose Classification

Faisal Bin Ashraf; Muhammad Usama Islam; Md Rayhan Kabir; Jasim Uddin

doi:10.1007/s42979-022-01618-8

YoNet: A Neural Network for Yoga Pose Classification

SN Comput Sci. 2023;4(2):198. doi: 10.1007/s42979-022-01618-8. Epub 2023 Feb 8.

Authors

Faisal Bin Ashraf¹, Muhammad Usama Islam², Md Rayhan Kabir³, Jasim Uddin⁴

Affiliations

¹ Department of Computer Science and Engineering, University of California, Riverside, CA USA.
² School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA USA.
³ Department of Computing Science, University of Alberta, Edmonton, AB Canada.
⁴ Department of Applied Computing and Engineering, Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, Wales, UK.

Abstract

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.

Keywords: Deep learning; Image classification; Neural network; Pose recognition; Yoga pose.