{"ID":2841451,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.10878","arxiv_id":"2511.10878","title":"Multi-Joint Physics-Informed Deep Learning Framework for Time-Efficient Inverse Dynamics","abstract":"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.","short_abstract":"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 pro...","url_abs":"https://arxiv.org/abs/2511.10878","url_pdf":"https://arxiv.org/pdf/2511.10878v1","authors":"[\"Shuhao Ma\",\"Zeyi Huang\",\"Yu Cao\",\"Wesley Doorsamy\",\"Chaoyang Shi\",\"Jun Li\",\"Zhi-Qiang Zhang\"]","published":"2025-11-14T01:15:10Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.HC\",\"eess.SP\"]","methods":"[]","has_code":false}