YoNet: A Neural Network for Yoga Pose Classification.

Autor: Ashraf FB; Department of Computer Science and Engineering, University of California, Riverside, CA USA., Islam MU; School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, LA USA., Kabir MR; Department of Computing Science, University of Alberta, Edmonton, AB Canada., Uddin J; Department of Applied Computing and Engineering, Cardiff School of Technologies, Cardiff Metropolitan University, Cardiff, Wales, UK.
Jazyk: angličtina
Zdroj: SN computer science [SN Comput Sci] 2023; Vol. 4 (2), pp. 198. Date of Electronic Publication: 2023 Feb 08.
DOI: 10.1007/s42979-022-01618-8
Abstrakt: 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.
Competing Interests: Conflict of interestThe authors declare that they have no conficts of interest in this research work.
(© The Author(s) 2023.)
Databáze: MEDLINE
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