Accurate understanding of muscle activation and muscle forces plays an essential role in neuro-rehabilitation and musculoskeletal disorder treatments. Computational musculoskeletal modeling has been widely used as a powerful non-invasive tool to estimate them through inverse dynamics using static optimization, but the inherent computational complexity results in time-consuming analysis. In this paper, we propose a knowledge-based deep learning framework for time-efficient inverse dynamic analysis, which can predict muscle activation and muscle forces from joint kinematic data directly while not requiring any label information during model training. The Bidirectional Gated Recurrent Unit (BiGRU) neural network is selected as the backbone of our model due to its proficient handling of time-series data. Prior physical knowledge from forward dynamics and pre-selected inverse dynamics based physiological criteria are integrated into the loss function to guide the training of neural networks. Experimental validations on two datasets, including one benchmark upper limb movement dataset and one self-collected lower limb movement dataset from six healthy subjects, are performed. The experimental results have shown that the selected BiGRU architecture outperforms other neural network models when trained using our specifically designed loss function, which illustrates the effectiveness and robustness of the proposed framework.

翻译：准确理解肌肉激活与肌肉力在神经康复与肌肉骨骼疾病治疗中具有重要作用。计算肌肉骨骼建模作为一种强大的非侵入性工具，已通过基于静态优化的逆动力学方法被广泛用于估计这些参数，但其固有的计算复杂性导致分析过程耗时。本文提出一种基于知识的深度学习框架，用于实现高效的逆动力学分析，该框架可直接从关节运动学数据预测肌肉激活与肌肉力，且在模型训练过程中无需任何标注信息。我们选择双向门控循环单元（BiGRU）神经网络作为模型主干，因其擅长处理时序数据。来自前向动力学的先验物理知识与基于预选逆动力学生理准则的约束被整合至损失函数中，用以指导神经网络的训练。我们在两个数据集上进行了实验验证，包括一个基准上肢运动数据集和一个从六名健康受试者自行采集的下肢运动数据集。实验结果表明，采用我们专门设计的损失函数训练时，所选BiGRU架构优于其他神经网络模型，这证明了所提框架的有效性与鲁棒性。