YoNet: A Neural Network for Yoga Pose Classification
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.
Published inSN Computer Science
VersionVoR (Version of Record)
CitationAshraf, 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.
Cardiff Met Affiliation
- Cardiff School of Technologies