Real-Time Yoga Pose Detection Desktop Application Using OpenPose and a Hybrid Machine – Deep Learning Framework with Enhanced Accuracy and Pose Coverage
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| Natura: | Recurso digital |
| Lingua: | inglese |
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Zenodo
2025
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| _version_ | 1866902206043652096 |
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| author | Pawale, Gangaprasad Raut, Rohan Bedre, Om Shaikh, Zaid Agrawal, Prof. Mayuri S. |
| author_facet | Pawale, Gangaprasad Raut, Rohan Bedre, Om Shaikh, Zaid Agrawal, Prof. Mayuri S. |
| contents | <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> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15249576 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Real-Time Yoga Pose Detection Desktop Application Using OpenPose and a Hybrid Machine – Deep Learning Framework with Enhanced Accuracy and Pose Coverage Pawale, Gangaprasad Raut, Rohan Bedre, Om Shaikh, Zaid Agrawal, Prof. Mayuri S. Yoga Pose Assessment, Deep Learning, Real-Time Pose Detection, OpenPose, Scoring Algorithm, Self-Guided Yoga Practice. <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> |
| title | Real-Time Yoga Pose Detection Desktop Application Using OpenPose and a Hybrid Machine – Deep Learning Framework with Enhanced Accuracy and Pose Coverage |
| topic | Yoga Pose Assessment, Deep Learning, Real-Time Pose Detection, OpenPose, Scoring Algorithm, Self-Guided Yoga Practice. |
| url | https://doi.org/10.5281/zenodo.15249576 |