Explainable quality assessment of effective aligned skeletal representations for martial arts movements by multi-machine learning decisions
Abstract How to utilize modern technological means to provide both accurate scoring and objective feedback for martial arts movements has become an issue that needs to be addressed in the field of physical education. This study proposes an intelligent scoring method based on machine learning. Firstl...
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-024-83475-4 |
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| author | Yiqun Pang Kaiqi Zhang Fengmei Li |
| author_facet | Yiqun Pang Kaiqi Zhang Fengmei Li |
| author_sort | Yiqun Pang |
| collection | DOAJ |
| description | Abstract How to utilize modern technological means to provide both accurate scoring and objective feedback for martial arts movements has become an issue that needs to be addressed in the field of physical education. This study proposes an intelligent scoring method based on machine learning. Firstly, the key features are extracted by the feature alignment technique, which eliminates the influence of athletes’ movement speed, rhythm and duration on the scoring, thus reflecting the athletes’ skill level more realistically. Second, to further improve the objectivity and accuracy, an adaptive weighted multi-model decision-making strategy is proposed. In addition, this study is the first to use interpretable artificial intelligence to provide feedback for teaching and learning Wushu. Experimental results indicate that the integrated model using the weighted average strategy not only outperforms other algorithms after feature alignment (on the XSQ dataset MAE is 0.237, RMSE is 0.442, sMAPE is 8.569, R $$^{2}$$ is 0.633, Pearson’s correlation is 0.807, ICC is 0.856. on the UMONS-Taichi dataset, MAE is 0.29, RMSE is 0.438, sMAPE is 10.01, R $$^{2}$$ is 0.844, Pearson correlation is 0.921, and ICC is 0.954. on the PBB dataset, MAE is 0.261, RMSE is 0.351, sMAPE is 6.765, R $$^{2}$$ is 0.557, Pearson correlation is 0.753, and ICC is 0.82), but is also close to the performance of human experts. In conclusion, this study not only achieves a performance of movement evaluation comparable to human experts, but also provides a technical framework for the rapid realizing of automatic scoring in other martial arts styles, which will promote the popularization and development of martial arts education. |
| format | Article |
| id | doaj-art-87db29d0336c4cf3acf929bd1d3c94bc |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-87db29d0336c4cf3acf929bd1d3c94bc2025-01-05T12:18:39ZengNature PortfolioScientific Reports2045-23222025-01-0115111610.1038/s41598-024-83475-4Explainable quality assessment of effective aligned skeletal representations for martial arts movements by multi-machine learning decisionsYiqun Pang0Kaiqi Zhang1Fengmei Li2School of Physical Education, Southwest Petroleum UniversitySchool of Dance and Martial Arts, Capital University of Physical Education and SportsSchool of Dance and Martial Arts, Capital University of Physical Education and SportsAbstract How to utilize modern technological means to provide both accurate scoring and objective feedback for martial arts movements has become an issue that needs to be addressed in the field of physical education. This study proposes an intelligent scoring method based on machine learning. Firstly, the key features are extracted by the feature alignment technique, which eliminates the influence of athletes’ movement speed, rhythm and duration on the scoring, thus reflecting the athletes’ skill level more realistically. Second, to further improve the objectivity and accuracy, an adaptive weighted multi-model decision-making strategy is proposed. In addition, this study is the first to use interpretable artificial intelligence to provide feedback for teaching and learning Wushu. Experimental results indicate that the integrated model using the weighted average strategy not only outperforms other algorithms after feature alignment (on the XSQ dataset MAE is 0.237, RMSE is 0.442, sMAPE is 8.569, R $$^{2}$$ is 0.633, Pearson’s correlation is 0.807, ICC is 0.856. on the UMONS-Taichi dataset, MAE is 0.29, RMSE is 0.438, sMAPE is 10.01, R $$^{2}$$ is 0.844, Pearson correlation is 0.921, and ICC is 0.954. on the PBB dataset, MAE is 0.261, RMSE is 0.351, sMAPE is 6.765, R $$^{2}$$ is 0.557, Pearson correlation is 0.753, and ICC is 0.82), but is also close to the performance of human experts. In conclusion, this study not only achieves a performance of movement evaluation comparable to human experts, but also provides a technical framework for the rapid realizing of automatic scoring in other martial arts styles, which will promote the popularization and development of martial arts education.https://doi.org/10.1038/s41598-024-83475-4 |
| spellingShingle | Yiqun Pang Kaiqi Zhang Fengmei Li Explainable quality assessment of effective aligned skeletal representations for martial arts movements by multi-machine learning decisions Scientific Reports |
| title | Explainable quality assessment of effective aligned skeletal representations for martial arts movements by multi-machine learning decisions |
| title_full | Explainable quality assessment of effective aligned skeletal representations for martial arts movements by multi-machine learning decisions |
| title_fullStr | Explainable quality assessment of effective aligned skeletal representations for martial arts movements by multi-machine learning decisions |
| title_full_unstemmed | Explainable quality assessment of effective aligned skeletal representations for martial arts movements by multi-machine learning decisions |
| title_short | Explainable quality assessment of effective aligned skeletal representations for martial arts movements by multi-machine learning decisions |
| title_sort | explainable quality assessment of effective aligned skeletal representations for martial arts movements by multi machine learning decisions |
| url | https://doi.org/10.1038/s41598-024-83475-4 |
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