The non-negative matrix factorization algorithm (NMF) decomposes a data matrix into a set of non-negative basis vectors, each scaled by a coefficient. In its original formulation, the NMF assumes the data samples and ...
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ISBN:
(纸本)9781424492701
The non-negative matrix factorization algorithm (NMF) decomposes a data matrix into a set of non-negative basis vectors, each scaled by a coefficient. In its original formulation, the NMF assumes the data samples and dimensions to be independently distributed, making it a lessthan-ideal algorithm for the analysis of time series data with temporal correlations. Here, we seek to derive an NMF that accounts for temporal dependencies in the data by explicitly incorporating a very simple temporal constraint for the coefficients into the NMF update rules. We applied the modified algorithm to 2 multi-dimensional electromyographic data sets collected from the human upper-limb to identify muscle synergies. We found that because it reduced the number of free parameters in the model, our modified NMF made it possible to use the Akaike Information Criterion to objectively identify a model order (i.e., the number of muscle synergies composing the data) that is more functionally interpretable, and closer to the numbers previously determined using ad hoc measures.
The non-negative matrix factorization algorithm (NMF) decomposes a data matrix into a set of non-negative basis vectors, each scaled by a coefficient. In its original formulation, the NMF assumes the data samples and ...
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ISBN:
(纸本)9781424492695
The non-negative matrix factorization algorithm (NMF) decomposes a data matrix into a set of non-negative basis vectors, each scaled by a coefficient. In its original formulation, the NMF assumes the data samples and dimensions to be independently distributed, making it a less-than-ideal algorithm for the analysis of time series data with temporal correlations. Here, we seek to derive an NMF that accounts for temporal dependencies in the data by explicitly incorporating a very simple temporal constraint for the coefficients into the NMF update rules. We applied the modified algorithm to 2 multi-dimensional electromyographic data sets collected from the human upper-limb to identify muscle synergies. We found that because it reduced the number of free parameters in the model, our modified NMF made it possible to use the Akaike Information Criterion to objectively identify a model order (i.e., the number of muscle synergies composing the data) that is more functionally interpretable, and closer to the numbers previously determined using ad hoc measures.
The purpose of this study was to investigate the effect of fatigue on selected lower extremity muscles synergy during running using non-negative matrix factorization algorithm method. Sixteen male recreational runners...
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The purpose of this study was to investigate the effect of fatigue on selected lower extremity muscles synergy during running using non-negative matrix factorization algorithm method. Sixteen male recreational runners participated in this study. The surface electromyographic activity of rectus femoris (RF), vastus lateralis (VL), vastus medialis (VM), biceps femoris (BF), semitendinosus, gastrocnemius medialis (GM), soleus (SO) and tibialis anterior (TA) were recorded on treadmill at 3.3 m s(-1) before and after the fatigue protocol. Synergy pattern and relative muscle weight were calculated by non-negativematrixfactorization (NNMF) algorithm method. The results showed that using the VAF method, five muscle synergies were extracted from the emg data during running. After the fatigue, the number of muscular synergies did not show a change, but relative weight of the muscles changed. Fatigue did not have any effect on the structure of muscular synergy, but changed the relative weight of muscles. These changes could be the strategy of the central nervous system to maintain optimal function of the motor system. (C) 2020 Elsevier Ltd. All rights reserved.
Musculoskeletal models (MMs) driven by electromyography (EMG) signals have been used to predict human movements. Muscle excitations of MMs are generally the amplitude of EMG, which shows large variability even when re...
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ISBN:
(纸本)9783031138225;9783031138218
Musculoskeletal models (MMs) driven by electromyography (EMG) signals have been used to predict human movements. Muscle excitations of MMs are generally the amplitude of EMG, which shows large variability even when repeating the same task. The general structure of muscle synergies has been proved to be consistent across test sessions, providing a perspective for extracting stable control information for MMs. Although non-negativematrixfactorization (NMF) is a common method for extracting synergies, the factorization result of NMF is not unique. In this study, we proposed an improved NMF algorithm for extracting stable control information of MMs to predict hand and wrist motions. Specifically, we supplemented the Hadamard product and L2-norm regularization term to the objective function of NMF. The proposed NMF was utilized to identify stable muscle synergies. Then, the time-varying profile of each synergy was fed into a subject-specific MM for estimating joint motions. The results demonstrated that the proposed scheme significantly outperformed a traditional MM and an MM combined with the classic NMF (NMF-MM), with averaged R and NRMSE equal to 0.89 +/- 0.06 and 0.16 +/- 0.04. Further, the similarity between muscle synergies extracted from different training data revealed the proposed method's effectiveness of identifying consistent control information for MMs. This study provides a novel model-based scheme for the estimation of continuous movements.
Objective. Musculoskeletal model (MM)-based myoelectric interface has aroused great interest in human-machine interaction. However, the performance of electromyography (EMG)-driven MM in long-term use would be degrade...
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Objective. Musculoskeletal model (MM)-based myoelectric interface has aroused great interest in human-machine interaction. However, the performance of electromyography (EMG)-driven MM in long-term use would be degraded owing to the inherent non-stationary characteristics of EMG signals. Here, to improve the estimation performance without retraining, we proposed a consistent muscle excitation extraction approach based on an improved non-negativematrixfactorization (NMF) algorithm for MM when applied to simultaneous hand and wrist movement prediction. Approach. We added constraints and L 2-norm regularization terms to the objective function of classic NMF regarding muscle weighting matrix and time-varying profiles, through which stable muscle synergies across days were identified. The resultant profiles of these synergies were then used to drive the MM. Both offline and online experiments were conducted to evaluate the performance of the proposed method in inter-day scenarios. Main results. The results demonstrated significantly better and more robust performance over several competitive methods in inter-day experiments, including machine learning methods, EMG envelope-driven MM, and classic NMF-based MM. Furthermore, the analysis of control information on different days revealed the effectiveness of the proposed method in obtaining consistent muscle excitations. Significance. The outcomes potentially provide a novel and promising pathway for the robust and zero-retraining control of myoelectric interfaces.
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