基于无标签sEMG信号的物理信息深度学习肌肉力预测

计算生物力学分析在理解和改善人类运动和功能方面发挥着关键作用。尽管基于物理的建模方法可以解释神经驱动与关节运动学之间的动态相互作用，但其计算延迟较高。近年来，数据驱动方法因其快速执行速度而成为有前景的替代方案，但在训练过程中仍需要标签信息，这在实际中难以获取。为此，本文提出一种 novel physics-informed deep learning method to predict muscle forces without any label information during model training. In addition, the proposed method could also identify personalized muscle-tendon parameters. To achieve this, the Hill muscle model-based forward dynamics is embedded into the deep neural network as the additional loss to further regulate the behavior of the deep neural network. Experimental validations on the wrist joint from six healthy subjects are performed, and a fully connected neural network (FNN) is selected to implement the proposed method. The predicted results of muscle forces show comparable or even lower root mean square error (RMSE) and higher coefficient of determination compared with baseline methods, which have to use the labeled surface electromyography (sEMG) signals, and it can also identify muscle-tendon parameters accurately, demonstrating the effectiveness of the proposed physics-informed deep learning method.