用部分肌电数据精准估算多自由度关节运动，还能还原未测肌肉的生理激活。
Physiologically Constrained Musculoskeletal Neural Network for Multi-DoF Joint Kinematics Estimation from Partially Observed sEMG
- 构建可微分的肌骨骼神经网络，融合卷积网络与生物力学前向模型。
- 在三种节奏动作和一种随机动作上误差更低，随机动作下性能提升显著。
- 能还原未测量肌肉的激活模式，参数保持生理合理，适合康复与假肢应用。
本文研究在部分表面肌电（sEMG）观测条件下多自由度（DoF）关节运动估计问题，即由于解剖限制或传感器约束，仅能测量部分任务相关肌肉。提出一种新型肌骨骼神经网络（MSK-NN），在估计多自由度关节角度的同时，推断已测与未测肌肉的激活状态。该模型由基于CNN的肌肉激活估计器与嵌入式肌骨骼前向动力学模块组成，形成全可微架构。不同于需额外生物力学标签（如肌腱力、关节力矩）的混合神经框架，MSK-NN无需内部生物力学变量的直接监督。设计了一种复合物理-生理损失函数，包含关节运动损失、数据驱动的肌肉协同损失以及解剖引导的趋势损失。方法在双自由度腕关节运动估计任务中评估，涵盖三种节奏性运动（速度与幅度无约束）及一种随机运动。相较于CNN、Bi-LSTM、CNN-LSTM与PET基线模型，MSK-NN在所有任务中均取得更低的归一化均方根误差（NRMSE）和更高的决定系数（R²），尤其在随机运动中表现更优。更重要的是，优化后的肌骨骼参数保持在生理范围内，且未输入肌肉的估计激活与实测肌电包络呈现强时间一致性，验证了该模型恢复生理合理激活的能力。
原文摘要 · Abstract (English)
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.
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