This paper investigates multi-degrees of freedom (DoF) joint kinematics estimation under partially observed surface electromyography (sEMG), where only a subset of task-relevant muscles can be measured due to anatomical inaccessibility or sensor constraints. A novel musculoskeletal neural network (MSK-NN) is proposed to estimate multi-DoF joint angles while simultaneously inferring activations for both measured and unmeasured muscles. MSK-NN consists of a CNN-based muscle activation estimator and an embedded MSK forward dynamics module, forming a fully differentiable architecture. Unlike existing hybrid neural frameworks that require additional biomechanical labels (e.g., muscle-tendon forces, joint torques), MSK-NN is trained without direct supervision of internal biomechanical variables. A composite physics-physiology loss is designed by incorporating a joint kinematics loss, a data-driven muscle synergy loss, and an anatomy-guided trend loss. The proposed method is evaluated on two-DoF wrist kinematics estimation across three rhythmic motions with unconstrained speed and amplitude, and one random motion. Compared with CNN, Bi-LSTM, CNN-LSTM, and PET baselines, MSK-NN achieves lower normalized root mean square error (NRMSE) and higher coefficient of determination (R2), especially for the random motion. More importantly, the optimized MSK parameters remain within physiological limits, and the estimated activation of an input-excluded muscle exhibits strong temporal agreement with its recorded sEMG envelope, demonstrating the capability of musculoskeletal (MSK)-NN to recover physiologically plausible activations.

翻译：本文研究了在部分观测表面肌电图（sEMG）条件下，由于解剖不可达性或传感器限制，仅能测量部分任务相关肌肉时的多自由度（DoF）关节运动学估计问题。提出了一种新型肌肉骨骼神经网络（MSK-NN），用于估计多自由度关节角度，同时推理已测量和未测量肌肉的激活状态。MSK-NN由基于CNN的肌肉激活估计器和嵌入的肌肉骨骼正向动力学模块构成，形成完全可微分的架构。与需要额外生物力学标签（如肌肉肌腱力、关节力矩）的现有混合神经框架不同，MSK-NN无需直接监督内部生物力学变量即可训练。通过整合关节运动学损失、数据驱动的肌肉协同损失和解剖引导趋势损失，设计了复合物理-生理损失函数。所提方法在三种具有非约束速度和幅度的节律运动及一种随机运动上，针对两自由度腕部运动学估计进行了评估。与CNN、Bi-LSTM、CNN-LSTM和PET基线方法相比，MSK-NN实现了更低的归一化均方根误差（NRMSE）和更高的决定系数（R²），尤其对随机运动表现更优。更重要的是，优化后的MSK参数保持在生理界限内，且输入排除肌肉的估计激活与其记录的sEMG包络呈现强时间一致性，证明了MSK-NN恢复生理可行激活的能力。