Exoskeletons and rehabilitation systems offer great potential for enhancing human strength and recovery through advanced human-machine interfaces (HMIs) that adapt to movement dynamics. However, the real-time application of physics-informed neural networks (PINNs) is limited by their reliance on fixed input lengths and surrogate models. This study introduces a novel physics-informed Gated Recurrent Network (PiGRN) designed to predict multi-joint torques using surface electromyography (sEMG) data. The PiGRN model employs a Gated Recurrent Unit (GRU) to convert time-series sEMG inputs into multi-joint kinematics and external loads, which are then integrated into an equation of motion to ensure consistency with physical laws. Experimental validation with sEMG data from five participants performing elbow flexion-extension tasks showed that the PiGRN model accurately predicted joint torques for 10 unfamiliar movements, with RMSE values between 4.02\% and 11.40\% and correlation coefficients ranging from 0.87 to 0.98. These findings highlight the PiGRN's potential for real-time exoskeleton and rehabilitation applications. Future research will explore more diverse datasets, improve musculoskeletal models, and investigate unsupervised learning methods.

翻译：外骨骼与康复系统通过适应运动动力学的先进人机界面（HMI），在增强人体力量与促进康复方面展现出巨大潜力。然而，物理信息神经网络（PINN）因其对固定输入长度和代理模型的依赖，在实时应用中受到限制。本研究提出了一种新颖的物理信息门控循环网络（PiGRN），旨在利用表面肌电信号（sEMG）数据预测多关节力矩。PiGRN模型采用门控循环单元（GRU），将时序sEMG输入转换为多关节运动学参数和外部载荷，随后将其整合至运动方程中以确保符合物理定律。通过五名受试者执行肘关节屈伸任务的sEMG数据进行实验验证，结果表明PiGRN模型能够准确预测10种陌生运动的关节力矩，其均方根误差（RMSE）介于4.02%至11.40%之间，相关系数范围为0.87至0.98。这些发现凸显了PiGRN在实时外骨骼与康复应用中的潜力。未来研究将探索更多样化的数据集、改进肌肉骨骼模型，并研究无监督学习方法。