无需标注数据，用物理约束模型快速预测多关节肌肉激活与力。
Multi-Joint Physics-Informed Deep Learning Framework for Time-Efficient Inverse Dynamics
- 引入跨关节注意力与双向循环单元捕捉关节间协调机制。
- 在无标签数据下性能媲美有监督方法，推理速度快。
- 适合临床评估与辅助设备控制，对多关节运动建模有效。
高效估计多关节系统中的肌肉激活与力量，对临床评估和辅助设备控制至关重要。然而，传统方法计算成本高，且缺乏高质量的多关节标注数据集。为此，我们提出一种物理信息深度学习框架，直接从运动学数据推断肌肉激活与力量。该框架采用新颖的多关节交叉注意力（MJCA）模块结合双向门控循环单元（BiGRU）层，捕捉关节间的协同作用，使每个关节能自适应融合其他关节的运动信息。通过将多关节动力学、关节耦合关系及外部力交互嵌入损失函数，所提出的物理信息MJCA-BiGRU（PI-MJCA-BiGRU）在无标签数据下实现生理上一致的预测，并支持高效推理。在两个数据集上的实验验证表明，其性能可媲美传统有监督方法，且无需真实标签；相比其他基线架构，MJCA模块显著提升了关节间协调建模能力。
原文摘要 · Abstract (English)
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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