Analysis of Disease-Induced Changes in Human Locomotor Patterns Through the Co-Joint Synergistic Attention Algorithm
Objective: Aiming to quantify and analyze disease-induced alterations in human movement, we explored the co-joint synergy patterns in locomotion through a vision-based co-joint synergistic attention algorithm. Methods: We recruited 30 participants (including 15 post-stroke patients and 15 healthy in...
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| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10974996/ |
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| author | Jingyao Chen Chen Wang Zeng-Guang Hou Pingye Deng Liang Peng Pu Zhang Ningcun Xu |
| author_facet | Jingyao Chen Chen Wang Zeng-Guang Hou Pingye Deng Liang Peng Pu Zhang Ningcun Xu |
| author_sort | Jingyao Chen |
| collection | DOAJ |
| description | Objective: Aiming to quantify and analyze disease-induced alterations in human movement, we explored the co-joint synergy patterns in locomotion through a vision-based co-joint synergistic attention algorithm. Methods: We recruited 30 participants (including 15 post-stroke patients and 15 healthy individuals) and extracted their 3D visual motor data for the joint feature coupling by a serial attention module. And we designed a dual-stream classification module for preclassification based on the spatio-temporal characteristics of the data. Then we extracted the important co-joint synergy patterns by a looping mask module and the co-joint synergy variability score. Results: Through the co-joint synergistic attention algorithm, we found significant differences in joint synergy patterns between post-stroke patients and healthy individuals during upper and lower limb tasks. Furthermore, we obtained quantitative results on the effect of specific diseases on co-joint synergy patterns among healthy individuals and patients. The validity of the result was verified by comparing with the commonly used Non-negative Matrix Factorization (NMF) and the Muscle Synergy Fractionation (MSF) methods. Conclusion: Specific diseases can cause changes in human movement patterns, and by the co-joint synergistic attention algorithm we can analyze the alterations in joint synergies and also quantify the importance of different synergy groups. Significance: This research proposes a new approach for identifying specific co-joint synergy patterns arising from disease-altered biomechanics, which provides a new targeted protocol for the rehabilitation process. |
| format | Article |
| id | doaj-art-98a9d2f3b3a04887af7d4dd6f82e2545 |
| institution | DOAJ |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-98a9d2f3b3a04887af7d4dd6f82e25452025-08-20T03:09:12ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102025-01-01331695170610.1109/TNSRE.2025.356346610974996Analysis of Disease-Induced Changes in Human Locomotor Patterns Through the Co-Joint Synergistic Attention AlgorithmJingyao Chen0https://orcid.org/0000-0002-4865-2118Chen Wang1https://orcid.org/0000-0001-9796-708XZeng-Guang Hou2https://orcid.org/0000-0002-1534-5840Pingye Deng3Liang Peng4https://orcid.org/0000-0001-6531-7517Pu Zhang5Ningcun Xu6https://orcid.org/0000-0001-9303-0302Faculty of Innovation Engineering, CASIA-MUST Joint Laboratory of Intelligence Science and Technology, Macau University of Science and Technology, Macau, ChinaState Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaState Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaInstitute of Analysis and Testing, Beijing Academy of Science and Technology, Beijing, ChinaState Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, ChinaFaculty of Innovation Engineering, CASIA-MUST Joint Laboratory of Intelligence Science and Technology, Macau University of Science and Technology, Macau, ChinaFaculty of Innovation Engineering, CASIA-MUST Joint Laboratory of Intelligence Science and Technology, Macau University of Science and Technology, Macau, ChinaObjective: Aiming to quantify and analyze disease-induced alterations in human movement, we explored the co-joint synergy patterns in locomotion through a vision-based co-joint synergistic attention algorithm. Methods: We recruited 30 participants (including 15 post-stroke patients and 15 healthy individuals) and extracted their 3D visual motor data for the joint feature coupling by a serial attention module. And we designed a dual-stream classification module for preclassification based on the spatio-temporal characteristics of the data. Then we extracted the important co-joint synergy patterns by a looping mask module and the co-joint synergy variability score. Results: Through the co-joint synergistic attention algorithm, we found significant differences in joint synergy patterns between post-stroke patients and healthy individuals during upper and lower limb tasks. Furthermore, we obtained quantitative results on the effect of specific diseases on co-joint synergy patterns among healthy individuals and patients. The validity of the result was verified by comparing with the commonly used Non-negative Matrix Factorization (NMF) and the Muscle Synergy Fractionation (MSF) methods. Conclusion: Specific diseases can cause changes in human movement patterns, and by the co-joint synergistic attention algorithm we can analyze the alterations in joint synergies and also quantify the importance of different synergy groups. Significance: This research proposes a new approach for identifying specific co-joint synergy patterns arising from disease-altered biomechanics, which provides a new targeted protocol for the rehabilitation process.https://ieeexplore.ieee.org/document/10974996/Co-joint synergy patternssynergistic attention algorithmrehabilitationdisease-induced alterations analysissynergy variability |
| spellingShingle | Jingyao Chen Chen Wang Zeng-Guang Hou Pingye Deng Liang Peng Pu Zhang Ningcun Xu Analysis of Disease-Induced Changes in Human Locomotor Patterns Through the Co-Joint Synergistic Attention Algorithm IEEE Transactions on Neural Systems and Rehabilitation Engineering Co-joint synergy patterns synergistic attention algorithm rehabilitation disease-induced alterations analysis synergy variability |
| title | Analysis of Disease-Induced Changes in Human Locomotor Patterns Through the Co-Joint Synergistic Attention Algorithm |
| title_full | Analysis of Disease-Induced Changes in Human Locomotor Patterns Through the Co-Joint Synergistic Attention Algorithm |
| title_fullStr | Analysis of Disease-Induced Changes in Human Locomotor Patterns Through the Co-Joint Synergistic Attention Algorithm |
| title_full_unstemmed | Analysis of Disease-Induced Changes in Human Locomotor Patterns Through the Co-Joint Synergistic Attention Algorithm |
| title_short | Analysis of Disease-Induced Changes in Human Locomotor Patterns Through the Co-Joint Synergistic Attention Algorithm |
| title_sort | analysis of disease induced changes in human locomotor patterns through the co joint synergistic attention algorithm |
| topic | Co-joint synergy patterns synergistic attention algorithm rehabilitation disease-induced alterations analysis synergy variability |
| url | https://ieeexplore.ieee.org/document/10974996/ |
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