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| Hauptverfasser: | , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2511.10878 |
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| _version_ | 1866918201631178752 |
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| author | Ma, Shuhao Huang, Zeyi Cao, Yu Doorsamy, Wesley Shi, Chaoyang Li, Jun Zhang, Zhi-Qiang |
| author_facet | Ma, Shuhao Huang, Zeyi Cao, Yu Doorsamy, Wesley Shi, Chaoyang Li, Jun Zhang, Zhi-Qiang |
| contents | Time-efficient estimation of muscle activations and forces across multi-joint systems is critical for clinical assessment and assistive device control. However, conventional approaches are computationally expensive and lack a high-quality labeled dataset for multi-joint applications. To address these challenges, we propose a physics-informed deep learning framework that estimates muscle activations and forces directly from kinematics. The framework employs a novel Multi-Joint Cross-Attention (MJCA) module with Bidirectional Gated Recurrent Unit (BiGRU) layers to capture inter-joint coordination, enabling each joint to adaptively integrate motion information from others. By embedding multi-joint dynamics, inter-joint coupling, and external force interactions into the loss function, our Physics-Informed MJCA-BiGRU (PI-MJCA-BiGRU) delivers physiologically consistent predictions without labeled data while enabling time-efficient inference. Experimental validation on two datasets demonstrates that PI-MJCA-BiGRU achieves performance comparable to conventional supervised methods without requiring ground-truth labels, while the MJCA module significantly enhances inter-joint coordination modeling compared to other baseline architectures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_10878 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Joint Physics-Informed Deep Learning Framework for Time-Efficient Inverse Dynamics Ma, Shuhao Huang, Zeyi Cao, Yu Doorsamy, Wesley Shi, Chaoyang Li, Jun Zhang, Zhi-Qiang Machine Learning Human-Computer Interaction Signal Processing Time-efficient estimation of muscle activations and forces across multi-joint systems is critical for clinical assessment and assistive device control. However, conventional approaches are computationally expensive and lack a high-quality labeled dataset for multi-joint applications. To address these challenges, we propose a physics-informed deep learning framework that estimates muscle activations and forces directly from kinematics. The framework employs a novel Multi-Joint Cross-Attention (MJCA) module with Bidirectional Gated Recurrent Unit (BiGRU) layers to capture inter-joint coordination, enabling each joint to adaptively integrate motion information from others. By embedding multi-joint dynamics, inter-joint coupling, and external force interactions into the loss function, our Physics-Informed MJCA-BiGRU (PI-MJCA-BiGRU) delivers physiologically consistent predictions without labeled data while enabling time-efficient inference. Experimental validation on two datasets demonstrates that PI-MJCA-BiGRU achieves performance comparable to conventional supervised methods without requiring ground-truth labels, while the MJCA module significantly enhances inter-joint coordination modeling compared to other baseline architectures. |
| title | Multi-Joint Physics-Informed Deep Learning Framework for Time-Efficient Inverse Dynamics |
| topic | Machine Learning Human-Computer Interaction Signal Processing |
| url | https://arxiv.org/abs/2511.10878 |