HierarchicalNets for multi level hierarchical classification of yoga poses
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Human pose estimation has long been an actively studied subject in computer vision (CV), with substantial applications in healthcare. It assists in evaluation and monitoring, enabling personalized rehabilitation programs and injury prevention. When applied to yoga, human pose estimation provides accurate alignment guidance throughout practice, reducing the risk of injury and promoting correct posture. This combination enhances physical therapy outcomes, supports overall wellness, and encourages the maintenance of a healthy and efficient yoga practice for general health improvement. We present a unique approach that combines cutting-edge vision transformer technologies with fine-grained hierarchical pose classification. Specifically, we introduce four hierarchical vision transformer architectures designed to incorporate hierarchical class label information into classification models. The proposed method aims to produce more precise classification results by identifying subtle differences between similar poses, thereby improving classification accuracy for yoga postures. The current state-of-the-art (SoTA) Top-1 accuracy for Level 1, 2, and 3 classification tasks is 89.81%, 85.10%, and 79.35%, respectively. Our proposed models surpass these benchmarks, achieving Top-1 accuracy scores of 96.79%, 95.64%, and 93.07%, respectively.
Date Issued
2026-08-11
Date Acceptance
2026-05-20
Citation
Scientific Reports, 2026, 16
ISSN
2045-2322
Publisher
Nature Portfolio
Journal / Book Title
Scientific Reports
Volume
16
Copyright Statement
© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/42218232
PII: 10.1038/s41598-026-54558-1
Publication Status
Published
Coverage Spatial
England
Article Number
24815
Date Publish Online
2026-05-30