An Approach using Skeleton-based Representations and Neural Networks for Yoga Pose Recognition
Amid a rapidly developing era, people can inevitably have problems with stress, depression, pressure, or difficulty sleeping due to frequent overthinking. To overcome the above problems, yoga will be an excellent solution to help adjust thoughts and harmonize body and soul, helping us relax, relax t...
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| Format: | Article |
| Language: | English |
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Sciendo
2025-01-01
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| Series: | Applied Computer Systems |
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| Online Access: | https://doi.org/10.2478/acss-2025-0009 |
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| author | Nguyen Hai Thanh Truong Nguyen Nhat Pham Linh Thuy Thi Pham Ngoc Huynh |
| author_facet | Nguyen Hai Thanh Truong Nguyen Nhat Pham Linh Thuy Thi Pham Ngoc Huynh |
| author_sort | Nguyen Hai Thanh |
| collection | DOAJ |
| description | Amid a rapidly developing era, people can inevitably have problems with stress, depression, pressure, or difficulty sleeping due to frequent overthinking. To overcome the above problems, yoga will be an excellent solution to help adjust thoughts and harmonize body and soul, helping us relax, relax the mind, and retain positive thoughts. Negative and evil auras will be pushed away, and the worldview will improve. Yoga practice has incorrectly caused many unwanted injuries for practitioners. Therefore, we present an approach grounded in skeleton-based feature extraction and neural networks to find a solution to the recognition of yoga postures, creating a premise for researching a smart virtual trainer that supports home workouts for users from input image data converted into skeleton data through MoveNet. The classification models were used to train recognition and classification of yoga poses. The models were trained and evaluated on a dataset of 3939 images of 10 yoga poses. Experimental results show that the proposed algorithms are entirely suitable for the classification task when achieving good results on different metrics such as Precision, Recall, F1-score, and Accuracy. |
| format | Article |
| id | doaj-art-d574fe54457d4220bc424f3237a2d413 |
| institution | Kabale University |
| issn | 2255-8691 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Computer Systems |
| spelling | doaj-art-d574fe54457d4220bc424f3237a2d4132025-08-20T03:49:59ZengSciendoApplied Computer Systems2255-86912025-01-01301758410.2478/acss-2025-0009An Approach using Skeleton-based Representations and Neural Networks for Yoga Pose RecognitionNguyen Hai Thanh0Truong Nguyen Nhat1Pham Linh Thuy Thi2Pham Ngoc Huynh3College of Information and Communication Technology, Can Tho University, Can Tho, VietnamCollege of Information and Communication Technology, Can Tho University, Can Tho, Vietnam3Faculty of Information Technology, Can Tho University of Technology, Can Tho, Vietnam4FPT University, FPT Polytechnic, Can Tho, VietnamAmid a rapidly developing era, people can inevitably have problems with stress, depression, pressure, or difficulty sleeping due to frequent overthinking. To overcome the above problems, yoga will be an excellent solution to help adjust thoughts and harmonize body and soul, helping us relax, relax the mind, and retain positive thoughts. Negative and evil auras will be pushed away, and the worldview will improve. Yoga practice has incorrectly caused many unwanted injuries for practitioners. Therefore, we present an approach grounded in skeleton-based feature extraction and neural networks to find a solution to the recognition of yoga postures, creating a premise for researching a smart virtual trainer that supports home workouts for users from input image data converted into skeleton data through MoveNet. The classification models were used to train recognition and classification of yoga poses. The models were trained and evaluated on a dataset of 3939 images of 10 yoga poses. Experimental results show that the proposed algorithms are entirely suitable for the classification task when achieving good results on different metrics such as Precision, Recall, F1-score, and Accuracy.https://doi.org/10.2478/acss-2025-0009classificationimage classificationmovenetpose recognitionskeleton dataskeleton-based featureyoga pose |
| spellingShingle | Nguyen Hai Thanh Truong Nguyen Nhat Pham Linh Thuy Thi Pham Ngoc Huynh An Approach using Skeleton-based Representations and Neural Networks for Yoga Pose Recognition Applied Computer Systems classification image classification movenet pose recognition skeleton data skeleton-based feature yoga pose |
| title | An Approach using Skeleton-based Representations and Neural Networks for Yoga Pose Recognition |
| title_full | An Approach using Skeleton-based Representations and Neural Networks for Yoga Pose Recognition |
| title_fullStr | An Approach using Skeleton-based Representations and Neural Networks for Yoga Pose Recognition |
| title_full_unstemmed | An Approach using Skeleton-based Representations and Neural Networks for Yoga Pose Recognition |
| title_short | An Approach using Skeleton-based Representations and Neural Networks for Yoga Pose Recognition |
| title_sort | approach using skeleton based representations and neural networks for yoga pose recognition |
| topic | classification image classification movenet pose recognition skeleton data skeleton-based feature yoga pose |
| url | https://doi.org/10.2478/acss-2025-0009 |
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