Evaluation of Methods for the Extraction of Spatial Muscle Synergies

Abstract

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.

Muscle synergies for evaluating upper limb in clinical applications: A systematic review

“…The essence of extracting muscle synergies is dimensionality reduction. Several matrix factorization methods have been applied, such as principal component analysis (PCA) [ 40 , [64] , [65] , [66] ], factor analysis (FA) [ 39 , 65 ], non-negative matrix factorization (NMF) [ 33 , 67 , 68 ], independent component analysis (ICA) [ 35 ], autoencoder (AE) [ 69 , 70 ], and second-order blind identification (SOBI) [ 71 ]. These methods assume different constraints on the input signals, and factorization results are affected by the noise level, signal characteristics, and the number of channels [ [72] , [73] , [74] ].…”

Evidence of synergy coordination patterns of upper-limb motor control in stroke patients with mild and moderate impairment

“…Despite the promising results when using muscle synergies as physiological markers to assess motor function ( Wang et al, 2020 ; Sheng et al, 2022 ), muscle synergies have not been fully investigated and widely used in clinical scenarios ( Zhao et al, 2023 ), and there is room for more evidence as how to use synergies as biomarkers is still an open question, as a few evidence are available and not always in agreement. Besides some subjective factors, such as experimental protocols and synergy analysis methods ( Steele et al, 2013 ; Banks et al, 2017 ; Zhao et al, 2022b ; Zhao et al, 2022c ), individual variability and subject- and task-specific muscle synergies make the outputs difficult to generalize and compare across studies ( Zhao et al, 2021 ; Cartier et al, 2022 ). To promote the use of muscle synergy analysis in clinical assessment and personalized therapy and deepen the understanding of the pathological mechanisms in post-stroke patients, this study focused on the alterations of synergy structure in post-stroke patients by synergy reconstruction analysis and hypothesized that stroke patients adopted specific synergy coordination patterns for upper-limb motor control as described in previous work ( Cheung et al, 2012 ) and coordination patterns were associated with the motor function.…”