Electromyography (EMG)-driven musculoskeletal modeling relies on high-quality measurements of muscle electrical activity to estimate muscle forces. However, a critical challenge for practical deployment of this approach is missing EMG data from muscles that contribute substantially to joint moments. This situation may arise due to either the inability to measure deep muscles with surface electrodes or the lack of a sufficient number of EMG channels. Muscle synergy analysis (MSA) is a dimensionality reduction approach that decomposes a large number of muscle excitations into a small number of time-varying synergy excitations along with time-invariant synergy weights that define the contribution of each synergy excitation to all muscle excitations. This study evaluates how well missing muscle excitations can be predicted using synergy excitations extracted from muscles with available EMG data (henceforth called “synergy extrapolation” or SynX). The method was evaluated using a gait data set collected from a stroke survivor walking on an instrumented treadmill at self-selected and fastest-comfortable speeds. The evaluation process started with full calibration of a lower-body EMG-driven model using 16 measured EMG channels (collected using surface and fine wire electrodes) per leg. One fine wire EMG channel (either iliopsoas or adductor longus) was then treated as unmeasured. The synergy weights associated with the unmeasured muscle excitation were predicted by solving a nonlinear optimization problem where the errors between inverse dynamics and EMG-driven joint moments were minimized. The prediction process was performed for different synergy analysis algorithms (principal component analysis and non-negative matrix factorization), EMG normalization methods, and numbers of synergies. SynX performance was most influenced by the choice of synergy analysis algorithm and number of synergies. Principal component analysis with five or six synergies consistently predicted unmeasured muscle excitations the most accurately and with the greatest robustness to EMG normalization method. Furthermore, the associated joint moment matching accuracy was comparable to that produced by initial EMG-driven model calibration using all 16 EMG channels per leg. SynX may facilitate the assessment of human neuromuscular control and biomechanics when important EMG signals are missing.

中文翻译：

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根据测量的肌肉协同作用预测未测量的肌肉兴奋的协同外推评估


肌电图 (EMG) 驱动的肌肉骨骼建模依赖于肌肉电活动的高质量测量来估计肌肉力量。然而，这种方法实际部署的一个关键挑战是缺少对关节力矩有重大贡献的肌肉的肌电图数据。这种情况的出现可能是由于无法使用表面电极测量深层肌肉或缺乏足够数量的肌电图通道。肌肉协同分析（MSA）是一种降维方法，它将大量的肌肉兴奋分解为少量的时变协同兴奋以及时不变的协同权重，该权重定义了每个协同兴奋对所有肌肉兴奋的贡献。这项研究评估了使用从肌肉中提取的协同兴奋和可用的肌电图数据（以下称为“协同外推”或 SynX）来预测缺失的肌肉兴奋的效果。该方法使用从中风幸存者收集的步态数据集进行评估，该数据集以自我选择和最快舒适的速度在仪表跑步机上行走。评估过程首先使用每条腿 16 个测量的 EMG 通道（使用表面和细线电极收集）对下半身 EMG 驱动模型进行全面校准。然后将一根细线肌电图通道（髂腰肌或长收肌）视为未测量。通过解决非线性优化问题来预测与未测量的肌肉兴奋相关的协同权重，其中逆动力学和肌电图驱动的关节力矩之间的误差被最小化。 针对不同的协同分析算法（主成分分析和非负矩阵分解）、EMG 归一化方法和协同数量进行预测过程。 SynX 性能受协同分析算法的选择和协同数量的影响最大。具有五种或六种协同作用的主成分分析能够最准确地预测未测量的肌肉兴奋，并且对肌电图标准化方法具有最大的鲁棒性。此外，相关的关节力矩匹配精度与使用每条腿的所有 16 个 EMG 通道进行初始 EMG 驱动模型校准所产生的精度相当。当重要的肌电图信号丢失时，SynX 可能有助于评估人类神经肌肉控制和生物力学。