Frame topology fusion-based hierarchical graph convolution for automatic assessment of physical rehabilitation exercises
Abstract Stroke rehabilitation movements are significantly influenced by patient subjectivity, leading to challenges in capturing subtle differences and temporal characteristics of patient motions. Existing methods typically focus on adjacent joint movements, overlooking the intricate interdependenc...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-12020-8 |
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| _version_ | 1849332839187742720 |
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| author | Shaohui Zhang Qiuying Han Peng Wang Junjie Li |
| author_facet | Shaohui Zhang Qiuying Han Peng Wang Junjie Li |
| author_sort | Shaohui Zhang |
| collection | DOAJ |
| description | Abstract Stroke rehabilitation movements are significantly influenced by patient subjectivity, leading to challenges in capturing subtle differences and temporal characteristics of patient motions. Existing methods typically focus on adjacent joint movements, overlooking the intricate interdependencies among body joints. Moreover, they lack the capacity to assess motion quality based on diverse temporal characteristics. To address these challenges, we propose a Frame Topology Fusion Hierarchical Graph Convolution Network (FTF-HGCN). This method aims to provide a more precise assessment of rehabilitation movement quality by effectively modeling both spatial and temporal features. First, this method combines nearby and distant keypoints to construct a fused topology structure for obtaining the enhanced motion representation. This allows the network to focus on joints with larger motion amplitudes. Second, based on the fused topology structure, a learnable topological matrix is established for each action frame to capture subtle differences between patient movements. Finally, a hierarchical temporal convolution attention module is employed to integrate motion feature information across different time sequences. Subsequently, a fully connected layer is used to output the predicted quality score of rehabilitation movements. Extensive experiments were conducted on KIMORE and UI-PRMD datasets, achieving best performance on relevant evaluation metrics (MAD: 13.4 $$\% \downarrow$$ , RMSE: 39.8 $$\% \downarrow$$ , MAPE: 7.6 $$\% \downarrow$$ ). This shows that the proposed FTF-HGCN method is capable of delivering accurate evaluations and offering superior support for the home-based rehabilitation of stroke patients. |
| format | Article |
| id | doaj-art-be5a99fb324b4a518dbd5ea5d88d1012 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-be5a99fb324b4a518dbd5ea5d88d10122025-08-20T03:46:05ZengNature PortfolioScientific Reports2045-23222025-07-0115111310.1038/s41598-025-12020-8Frame topology fusion-based hierarchical graph convolution for automatic assessment of physical rehabilitation exercisesShaohui Zhang0Qiuying Han1Peng Wang2Junjie Li3School of Artificial Intelligence, Zhoukou Normal UniversitySchool of Computer Science and Technology, Zhoukou Normal UniversitySchool of Artificial Intelligence, Zhoukou Normal UniversitySchool of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical UniversityAbstract Stroke rehabilitation movements are significantly influenced by patient subjectivity, leading to challenges in capturing subtle differences and temporal characteristics of patient motions. Existing methods typically focus on adjacent joint movements, overlooking the intricate interdependencies among body joints. Moreover, they lack the capacity to assess motion quality based on diverse temporal characteristics. To address these challenges, we propose a Frame Topology Fusion Hierarchical Graph Convolution Network (FTF-HGCN). This method aims to provide a more precise assessment of rehabilitation movement quality by effectively modeling both spatial and temporal features. First, this method combines nearby and distant keypoints to construct a fused topology structure for obtaining the enhanced motion representation. This allows the network to focus on joints with larger motion amplitudes. Second, based on the fused topology structure, a learnable topological matrix is established for each action frame to capture subtle differences between patient movements. Finally, a hierarchical temporal convolution attention module is employed to integrate motion feature information across different time sequences. Subsequently, a fully connected layer is used to output the predicted quality score of rehabilitation movements. Extensive experiments were conducted on KIMORE and UI-PRMD datasets, achieving best performance on relevant evaluation metrics (MAD: 13.4 $$\% \downarrow$$ , RMSE: 39.8 $$\% \downarrow$$ , MAPE: 7.6 $$\% \downarrow$$ ). This shows that the proposed FTF-HGCN method is capable of delivering accurate evaluations and offering superior support for the home-based rehabilitation of stroke patients.https://doi.org/10.1038/s41598-025-12020-8 |
| spellingShingle | Shaohui Zhang Qiuying Han Peng Wang Junjie Li Frame topology fusion-based hierarchical graph convolution for automatic assessment of physical rehabilitation exercises Scientific Reports |
| title | Frame topology fusion-based hierarchical graph convolution for automatic assessment of physical rehabilitation exercises |
| title_full | Frame topology fusion-based hierarchical graph convolution for automatic assessment of physical rehabilitation exercises |
| title_fullStr | Frame topology fusion-based hierarchical graph convolution for automatic assessment of physical rehabilitation exercises |
| title_full_unstemmed | Frame topology fusion-based hierarchical graph convolution for automatic assessment of physical rehabilitation exercises |
| title_short | Frame topology fusion-based hierarchical graph convolution for automatic assessment of physical rehabilitation exercises |
| title_sort | frame topology fusion based hierarchical graph convolution for automatic assessment of physical rehabilitation exercises |
| url | https://doi.org/10.1038/s41598-025-12020-8 |
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