Multi-Joint Physics-Informed Deep Learning Framework for Time-Efficient Inverse Dynamics Article Swipe

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Ma, Shuhao , Huang, Zeyi , Cao Yu , Doorsamy Wesley , Shi Chaoyang , Li Jun , Zhang, Zhi-Qiang · YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2511.10878

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.

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preprint

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http://arxiv.org/abs/2511.10878

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https://arxiv.org/pdf/2511.10878

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green

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https://openalex.org/W4416336182

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https://openalex.org/W4416336182

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DOI

https://doi.org/10.48550/arxiv.2511.10878

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Title

Multi-Joint Physics-Informed Deep Learning Framework for Time-Efficient Inverse Dynamics

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preprint

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Publication year

2025

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2025-11-14

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Authors

Ma, Shuhao, Huang, Zeyi, Cao Yu, Doorsamy Wesley, Shi Chaoyang, Li Jun, Zhang, Zhi-Qiang

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https://arxiv.org/abs/2511.10878

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https://arxiv.org/pdf/2511.10878

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Yes

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green

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