Quantifying movement variability is a crucial aspect for clinical and laboratory investigations in several contexts. However, very few studies have assessed, in detail, the intra-subject variability across movements and the inter-subject variability. Muscle synergies are a valuable method that can be used to assess such variability. In this study, we assess, in detail, intra-subject and inter-subject variability in a scenario based on a comprehensive dataset, including multiple repetitions of multi-directional reaching movements. The results show that muscle synergies are a valuable tool for quantifying variability at the muscle level and reveal that intra-subject variability is lower than inter-subject variability in synergy modules and related temporal coefficients, and both intra-subject and inter-subject similarity are higher than random synergy matching, confirming shared underlying control structures. The study deepens the available knowledge on muscle synergy-based motor function assessment and rehabilitation applications, discussing their applicability to real scenarios.

Muscle synergy has been applied to comprehend how the central nervous system (CNS) controls movements for decades. However, it is not clear about the motion control mechanism and the relationship between motions and muscle synergies. In this paper, we designed two experiments to corroborate the hypothesis: (1) motions can be decomposed to motion primitives, which are driven by muscle synergy primitives and (2) variations of motion primitives in direction and scale are modulated by activation coefficients rather than muscle synergy primitives. Surface electromyographic (EMG) signals were recorded from nine muscles of the upper limb. Nonnegative matrix factorization (NMF) was applied to extract muscle synergy vectors and corresponding activation coefficients. We found that synergy structures of different movement patterns were similar (α=0.05). The motion modulation indexes (MMI) among movement patterns in reaching movements showed apparent differences. Merging coefficients and reconstructed similarity of synergies between simple motions and complex motions were significant. This study revealed the motion control mechanism of the CNS and provided a rehabilitation and evaluation method for patients with motor dysfunction in exercise and neuroscience.

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Remaining useful life (RUL) plays an important role in prognostic and health management to reduce maintenance costs and avoid possible accidents. Massive multi‐sensor data makes it challenging to extract degradation features and predict RUL. This paper develops a novel RUL prediction framework consisted of a multi‐sensor fusion model and a hybrid prediction model. HI curves are constructed by synthesizing multiple metrics including time correlation, monotonicity, robustness, and consistency, making our data fusion method different from existing methods considering only one metric. In many practical situations, the initial degradation levels of obtained HI curves are different, and treating all curves without classification will result in errors in RUL prediction. The K‐means method is used to partition degradation paths into discrete states and the obtained HI curves are grouped by their initial states and then trained separately. A hybrid prediction model combining Long Short‐Term Memory (LSTM) network and Support Vector Regression (SVR) is developed to predict the RUL. The dataset of turbofan engines is used to verify the proposed method. The results show that the proposed method performs better than many existing methods.

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Objective. Due to the variability of human movements, muscle activations vary among trials and subjects. However, few studies investigated how data organization methods for addressing variability impact the extracted muscle synergies. Approach. Fifteen healthy subjects performed a large set of upper limb multi-directional point-to-point reaching movements. Then, the study extracted muscle synergies under different data settings and investigated how data structure prior to synergy extraction, namely concatenation, averaging, and single trial, the number of considered trials, and the number of reaching directions affected the number and components of muscle synergies. Main results. The results showed that the number and components of synergies were significantly affected by the data structure. The concatenation method identified the highest number of synergies, and the averaging method usually found a smaller number of synergies. When the concatenated trials or reaching directions was lower than a minimum value, the number of synergies increased with the increase of the number of trials or reaching directions; however, when the number of trials or reaching directions reached a threshold, the number of synergies was usually constant or with less variation even when novel directions and trials were added. Similarity analysis also showed a slight increase when the number of trials or reaching directions was lower than a threshold. This study recommends that at least five trials and four reaching directions and the concatenation method are considered in muscle synergies analysis during upper limb tasks. Significance. This study makes the researchers focus on the variability analysis induced by the diseases rather than the techniques applied for synergies analysis and promotes applications of muscle synergies in clinical scenarios.

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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.