Zhao Kunkun, Wen Haiying, Zhang Zhisheng, Atzori Manfredo, Müller Henning, Xie Zhongqu, Scano Alessandro
School of Mechanical Engineering, Southeast University, Nanjing, China.
Engineering Research Center of New Light Sources Technology and Equipment, Ministry of Education, Nanjing, China.
Front Neurosci. 2022 Jun 2;16:732156. doi: 10.3389/fnins.2022.732156. eCollection 2022.
Muscle synergies have been largely used in many application fields, including motor control studies, prosthesis control, movement classification, rehabilitation, and clinical studies. Due to the complexity of the motor control system, the full repertoire of the underlying synergies has been identified only for some classes of movements and scenarios. Several extraction methods have been used to extract muscle synergies. However, some of these methods may not effectively capture the nonlinear relationship between muscles and impose constraints on input signals or extracted synergies. Moreover, other approaches such as autoencoders (AEs), an unsupervised neural network, were recently introduced to study bioinspired control and movement classification. In this study, we evaluated the performance of five methods for the extraction of spatial muscle synergy, namely, principal component analysis (PCA), independent component analysis (ICA), factor analysis (FA), nonnegative matrix factorization (NMF), and AEs using simulated data and a publicly available database. To analyze the performance of the considered extraction methods with respect to several factors, we generated a comprehensive set of simulated data (ground truth), including spatial synergies and temporal coefficients. The signal-to-noise ratio (SNR) and the number of channels (NoC) varied when generating simulated data to evaluate their effects on ground truth reconstruction. This study also tested the efficacy of each synergy extraction method when coupled with standard classification methods, including K-nearest neighbors (KNN), linear discriminant analysis (LDA), support vector machines (SVM), and Random Forest (RF). The results showed that both SNR and NoC affected the outputs of the muscle synergy analysis. Although AEs showed better performance than FA in variance accounted for and PCA in synergy vector similarity and activation coefficient similarity, NMF and ICA outperformed the other three methods. Classification tasks showed that classification algorithms were sensitive to synergy extraction methods, while KNN and RF outperformed the other two methods for all extraction methods; in general, the classification accuracy of NMF and PCA was higher. Overall, the results suggest selecting suitable methods when performing muscle synergy-related analysis.
肌肉协同在许多应用领域中得到了广泛应用，包括运动控制研究、假肢控制、运动分类、康复和临床研究。由于运动控制系统的复杂性，仅在某些类型的运动和场景中确定了潜在协同的全部组成。已经使用了几种提取方法来提取肌肉协同。然而，其中一些方法可能无法有效捕捉肌肉之间的非线性关系，并且对输入信号或提取的协同施加约束。此外，最近还引入了其他方法，如自动编码器（AE），一种无监督神经网络，来研究生物启发控制和运动分类。在本研究中，我们使用模拟数据和一个公开可用的数据库评估了五种空间肌肉协同提取方法的性能，即主成分分析（PCA）、独立成分分析（ICA）、因子分析（FA）、非负矩阵分解（NMF）和AE。为了分析所考虑的提取方法在几个因素方面的性能，我们生成了一组全面的模拟数据（真实情况），包括空间协同和时间系数。在生成模拟数据时，信噪比（SNR）和通道数（NoC）会发生变化，以评估它们对真实情况重建的影响。本研究还测试了每种协同提取方法与标准分类方法（包括K近邻（KNN）、线性判别分析（LDA）、支持向量机（SVM）和随机森林（RF））结合时的有效性。结果表明，SNR和NoC都影响肌肉协同分析的输出。尽管AE在方差占比方面表现优于FA，在协同向量相似度和激活系数相似度方面优于PCA，但NMF和ICA的表现优于其他三种方法。分类任务表明，分类算法对协同提取方法敏感，而对于所有提取方法，KNN和RF的表现优于其他两种方法；总体而言，NMF和PCA的分类准确率更高。总体而言，结果表明在进行与肌肉协同相关的分析时应选择合适的方法。
