Improving Aerobics Posture Evaluation by Transfer Learning: Humanized Computational Application of BERT-PTA Domain Adaptive Methods
Abstract Due to the influence of datasets, traditional pose evaluation methods have insufficient generalization ability, high computational resource requirements, and low efficiency. To address this issue, this article applied transfer learning into the field of aerobics posture evaluation and achie...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-05-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-025-00867-8 |
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| Summary: | Abstract Due to the influence of datasets, traditional pose evaluation methods have insufficient generalization ability, high computational resource requirements, and low efficiency. To address this issue, this article applied transfer learning into the field of aerobics posture evaluation and achieved automation and objectivity of posture evaluation through BERT-PTA (Bidirectional Encoder Representations from Transformers-Prototype-based Transfer Assistants) domain adaptive methods. BERT and PTA methods were chosen because BERT’s bidirectional language understanding and transfer learning capabilities can effectively adapt to the language instructions in the field of aerobics, and PTA’s posture tracking and humanized computing features provide an accurate and user-friendly solution specifically for the assessment of aerobics poses. First, a BERT-PTA model was established based on the collection of aerobics posture data. Second, the BERT-PTA model was used to extract features from the preprocessed posture data. Next, a convolutional neural network was used to construct a key point localization model for aerobics poses, and transfer learning was used to train and fine-tune the model. Finally, experimental verification was conducted on using transfer learning to improve aerobics posture evaluation. The results showed that the precision of using transfer learning to improve aerobics posture evaluation was 4.88% and 8.86% higher than that of the other two methods, respectively. The recall rate of using transfer learning to improve aerobics posture evaluation was 3.45% and 7.14% higher than that of the other two methods, respectively. The evaluation efficiency of using transfer learning to improve aerobics posture evaluation was 6.52–7.69% higher than that of the other two methods, respectively. In short, using transfer learning to improve aerobics can provide more scientific guidance for aerobics sports. |
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| ISSN: | 1875-6883 |