Muscle force and joint kinematics estimation from surface electromyography (sEMG) are essential for real-time biomechanical analysis of the dynamic interplay among neural muscle stimulation, muscle dynamics, and kinetics. Recent advances in deep neural networks (DNNs) have shown the potential to improve biomechanical analysis in a fully automated and reproducible manner. However, the small sample nature and physical interpretability of biomechanical analysis limit the applications of DNNs. This paper presents a novel physics-informed low-shot learning method for sEMG-based estimation of muscle force and joint kinematics. This method seamlessly integrates Lagrange's equation of motion and inverse dynamic muscle model into the generative adversarial network (GAN) framework for structured feature decoding and extrapolated estimation from the small sample data. Specifically, Lagrange's equation of motion is introduced into the generative model to restrain the structured decoding of the high-level features following the laws of physics. And a physics-informed policy gradient is designed to improve the adversarial learning efficiency by rewarding the consistent physical representation of the extrapolated estimations and the physical references. Experimental validations are conducted on two scenarios (i.e. the walking trials and wrist motion trials). Results indicate that the estimations of the muscle forces and joint kinematics are unbiased compared to the physics-based inverse dynamics, which outperforms the selected benchmark methods, including physics-informed convolution neural network (PI-CNN), vallina generative adversarial network (GAN), and multi-layer extreme learning machine (ML-ELM).

翻译：从表面肌电信号(sEMG)中估计肌肉力和关节运动学对于实时生物力学分析神经肌肉刺激、肌肉动力学与动力学之间的动态相互作用至关重要。深度神经网络(DNNs)的最新进展已展现出以全自动且可重复的方式改善生物力学分析的潜力。然而，生物力学分析的小样本特性和物理可解释性限制了DNNs的应用。本文提出了一种新颖的物理信息小样本学习方法，用于基于sEMG的肌肉力和关节运动学估计。该方法将拉格朗日运动方程和逆动力学肌肉模型无缝集成到生成对抗网络(GAN)框架中，以实现结构化特征解码和小样本数据的外推估计。具体而言，拉格朗日运动方程被引入生成模型，以约束高级特征的结构化解码遵循物理规律。同时，设计了一种物理信息策略梯度，通过奖励外推估计与物理参考之间一致的物理表示来提高对抗学习效率。实验验证在两个场景(即步行试验和手腕运动试验)中进行。结果表明，与基于物理的逆动力学相比，肌肉力和关节运动学的估计是无偏的，其性能优于选定的基准方法，包括物理信息卷积神经网络(PI-CNN)、原始生成对抗网络(GAN)和多层极限学习机(ML-ELM)。