Збережено в:
| Автори: | , , , , |
|---|---|
| Формат: | Recurso digital |
| Мова: | Англійська |
| Опубліковано: |
Zenodo
2025
|
| Предмети: | |
| Онлайн доступ: | https://doi.org/10.5281/zenodo.15249576 |
| Теги: |
Додати тег
Немає тегів, Будьте першим, хто поставить тег для цього запису!
|
Зміст:
- <p><em><span lang="EN-US">This work introduces a novel method for evaluating various Yoga poses using advanced deep learning techniques. In our approach, a conventional PC camera is employed to capture real-time video, and multi-part pose detection is applied to identify the Yoga posture. Once a pose is detected, the system utilizes an improved scoring algorithm that is designed to provide consistent assessment across all types of poses. The proposed method supports self-guided Yoga practice by offering immediate feedback on pose accuracy, thereby assisting users in refining their technique. To validate the performance of our system, we conducted extensive experiments on a diverse set of Yoga poses across different environmental settings, which demonstrated its robustness and adaptability. Furthermore, we developed a hybrid machine learning framework that incorporates linear regression for extracting meaningful features from the key-points identified by OpenPose in each video frame. This integration not only enhances the recognition accuracy but also facilitates the real-time analysis of Yoga poses. Overall, our approach presents a comprehensive solution for Yoga pose assessment, paving the way for more effective and accessible self-learning Yoga applications.</span></em></p>