高重建质量与重启可重复性不保证恢复真实肌肉协同模式
High Reconstruction Quality and Restart Repeatability Do Not Guarantee Recovery of Ground-Truth Muscle Synergies
- Research Academy of Grand Health, Faculty of Sports Sciences, Ningbo University（宁波大学体育科学学院大健康研究院）
- School of Information Technology and Artificial Intelligence, Zhejiang University of Finance and Economics（浙江财经大学信息与人工智能学院）
- National and Local Joint Engineering Research Centre of Rehabilitation Medical Technology（福建中医药大学康复医学院康复医疗技术国家地方联合工程研究中心）
- College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine（清华大学电子工程系）
- Department of Electronic Engineering, Tsinghua University
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
本研究通过大规模合成数据检验发现，高重建质量和重启可重复性不能保证正确恢复肌肉协同的秩和空间向量，需谨慎解释协同分析结果。
中文摘要 AI 辅助
高重建质量和多次拟合间的一致性并不必然意味着肌肉协同模式的恢复。我们检验了方差解释率（VAF）/肘部法则能否恢复生成协同的数量和空间向量，高重启可重复性是否表明恢复成功，以及五个设计因素如何影响恢复。将非负矩阵分解应用于4,320个合成的16肌肉数据集，这些数据集在生成秩、噪声、试验次数、空间相似性和激活重叠方面各不相同。综合恢复要求正确秩且每个匹配空间向量的余弦相似度至少为0.80。使用探索性异方差Wald检验（经Benjamini-Hochberg校正）评估因素效应和双因素交互作用。秩选择在17.6%的数据集中精确，在54.9%中过低，在27.5%中过高；综合恢复率为13.9%。在VAF至少为0.90的拟合中，仅11.3%实现综合恢复。在3,762个空间可重复性至少为0.95的数据集中，19.6%具有正确秩，15.7%实现综合恢复。所有五个因素均与恢复相关（校正后p < 0.001）。恢复率随空间相似性增加从26.2%降至1.7%，随激活重叠增加从26.2%降至2.2%。秩为7-9时的恢复率低于秩为3-5时，从3次试验的11.0%增至80次试验的15.8%，并随噪声非单调变化。由实测sEMG参考因子合成的五个无噪声信号也显示欠选择，尽管VAF高于0.918。在该选择器下，高重建质量和重启一致性不足以指示正确秩和空间恢复。肌肉协同解释应考虑秩敏感性以及空间和激活模式的可分离性。
英文摘要
High reconstruction quality and agreement across repeated fits do not necessarily establish recovery of muscle synergies. We tested whether a variance-accounted-for (VAF)/elbow rule recovers the generating synergy count and spatial vectors, whether high restart repeatability indicates recovery, and how five design factors affect recovery. Non-negative matrix factorisation was applied to 4,320 synthetic 16-muscle datasets varying generating rank, noise, trial count, spatial similarity and activation overlap. Combined recovery required the correct rank and cosine similarity of at least 0.80 for every matched spatial vector. Factor effects and two-factor interactions were assessed using exploratory heteroscedastic Wald tests with Benjamini-Hochberg adjustment. Rank selection was exact in 17.6% of datasets, too low in 54.9% and too high in 27.5%; combined recovery was 13.9%. Among fits with VAF at least 0.90, only 11.3% achieved combined recovery. Among 3,762 datasets with spatial repeatability at least 0.95, 19.6% had the correct rank and 15.7% achieved combined recovery. All five factors were associated with recovery (adjusted p < 0.001). Recovery declined from 26.2% to 1.7% with increasing spatial similarity and from 26.2% to 2.2% with increasing activation overlap. It was lower at ranks 7-9 than at 3-5, increased from 11.0% with 3 trials to 15.8% with 80 trials, and varied non-monotonically with noise. Five noiseless signals synthesised from measured-sEMG reference factors also showed under-selection despite VAF above 0.918. Under this selector, high reconstruction quality and restart agreement were insufficient indicators of correct rank and spatial recovery. Muscle-synergy interpretation should account for rank sensitivity and the separability of spatial and activation patterns.
