Applying Deep Learning Networks to Identify Optimized Paths in Gymnastic Movement Techniques
The study adopts the OpenPose algorithm in deep learning to extract and recognize gymnastics movements, and it initially constructs the OpenPose gymnastics movement recognition model. The MobileNet-V3 network is introduced to replace VGG-19, which was the feature extraction network in the original m...
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| Vydané v: | Applied mathematics and nonlinear sciences Ročník 10; číslo 1 |
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| Hlavní autori: | , , |
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Beirut
Sciendo
01.01.2025
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services |
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| ISSN: | 2444-8656, 2444-8656 |
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| Abstract | The study adopts the OpenPose algorithm in deep learning to extract and recognize gymnastics movements, and it initially constructs the OpenPose gymnastics movement recognition model. The MobileNet-V3 network is introduced to replace VGG-19, which was the feature extraction network in the original model, in order to optimize the accuracy of OpenPose in recognizing gymnastics actions and to construct an OpenPose-MobileNet-V3 gymnastics action recognition model. The original model is compared with the optimized OpenPose-MobileNet-V3 model for comparison experiments in action recognition, and then the OpenPose-MobileNet-V3 model is compared with other recognition models to examine its effectiveness in action recognition. Finally, the parameter sensitivities of MobileNet-V3 and cosine annealing strategies are compared to explore the optimization effect of the two strategies on the OpenPose model.The OpenPose-MobileNet-V3 algorithm improves its recognition accuracy by 6.857% over the pre-optimization OpenPose algorithm.The recognition accuracy of the OpenPose-MobileNet-V3 is improved by 6.857% on the two datasets, which have accuracies of 95.786% and 94.572%, respectively, which are significantly better than other recognition models. The cosine annealing strategy-trained model is 2.143 percentage points less accurate than the OpenPose-MobileNet-V3 model at recognizing gymnastics movements, and MobileNet-V3 is better optimized. |
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| AbstractList | The study adopts the OpenPose algorithm in deep learning to extract and recognize gymnastics movements, and it initially constructs the OpenPose gymnastics movement recognition model. The MobileNet-V3 network is introduced to replace VGG-19, which was the feature extraction network in the original model, in order to optimize the accuracy of OpenPose in recognizing gymnastics actions and to construct an OpenPose-MobileNet-V3 gymnastics action recognition model. The original model is compared with the optimized OpenPose-MobileNet-V3 model for comparison experiments in action recognition, and then the OpenPose-MobileNet-V3 model is compared with other recognition models to examine its effectiveness in action recognition. Finally, the parameter sensitivities of MobileNet-V3 and cosine annealing strategies are compared to explore the optimization effect of the two strategies on the OpenPose model.The OpenPose-MobileNet-V3 algorithm improves its recognition accuracy by 6.857% over the pre-optimization OpenPose algorithm.The recognition accuracy of the OpenPose-MobileNet-V3 is improved by 6.857% on the two datasets, which have accuracies of 95.786% and 94.572%, respectively, which are significantly better than other recognition models. The cosine annealing strategy-trained model is 2.143 percentage points less accurate than the OpenPose-MobileNet-V3 model at recognizing gymnastics movements, and MobileNet-V3 is better optimized. |
| Author | Mo, Dan Wang, Yintong Zhang, Bowen |
| Author_xml | – sequence: 1 givenname: Dan surname: Mo fullname: Mo, Dan email: momo0909415@163.com organization: College of Sports Arts, Jilin Sport University, Changchun, Jilin, 130022, China – sequence: 2 givenname: Yintong surname: Wang fullname: Wang, Yintong organization: Graduate Office, Jilin Sport University, Changchun, Jilin, 130022, China – sequence: 3 givenname: Bowen surname: Zhang fullname: Zhang, Bowen organization: Department of Physical Education, Hebei Software Vocational and Technical College, Baoding, Hebei, 071000, China |
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| Cites_doi | 10.1016/j.humov.2014.01.001 10.14569/IJACSA.2024.0150113 10.3390/bioengineering9060261 10.1016/j.robot.2021.103830 10.1109/TMM.2022.3232034 10.1007/s00521-020-05632-w 10.1109/ACCESS.2018.2890150 10.1145/3343031.3350910 10.1109/TCBB.2014.2343960 10.52783/jes.1387 10.3390/app9020226 10.3390/app12104847 10.1016/j.jvcir.2024.104227 10.1109/ACCESS.2018.2817253 10.1016/j.eswa.2023.121978 10.1155/2021/1215065 10.1109/JSEN.2021.3114758 10.1007/978-3-031-62881-8_19 10.1016/j.bdr.2024.100477 |
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| Title | Applying Deep Learning Networks to Identify Optimized Paths in Gymnastic Movement Techniques |
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