Otherwise, to reduce error, instead of selecting a large number of muscles can be used less muscles to record signals and can be increased the number of iterations and then can be selected a percentage of the best results. However, it is not clear about the motion control mechanism and the. If there is less possibility of repetition due to online processing limitations, to reduce error, it is necessary to increase the number of muscle signals. Muscle synergy has been applied to comprehend how the central nervous system (CNS) controls movements for decades. The results also showed a large consistency of the three extracted muscle synergies, for both synergy activation coefficients (mean r max values range from 0.87 to 0.97) and muscle synergy vectors (mean r values range from 0. However by selecting a percentage of the best results, mean and standard deviation of RMSE values are reduced significantly, and mean of RMSE values with increasing number of signals trend to increase. In addition, in both cases of signals with and without noise with increased number of signals, mean of RMSE values tend to decrease. All processes are performed on signals with and without noise, considering the signal to noise ratio between 0 and 20 dB.According to the results, the reconstruction error in signals without noise was very small and was increased by noise addition. We investigated the effect of number of muscle activation signals on the efficiency of muscle synergy extraction. The algorithm of non-negative matrix factorization is used in extracting the muscle synergy and reconstructing signals. (A) The value of variability accounted for (VAF) is 80.67. More precisely, the variability in EMG patterns was mainly reflected in the temporal components of muscle synergies: each synergy activation coefficient (H s) of UL showed important time shifts between participants. Muscle synergies matrix and synergy activation coefficient under different module amounts during wrist flexion (WF) movement. In this study, simulated muscle activation signals are produced by the combination of wavelets, similar to the real signals. The results showed a higher variability of the UL in kinematics, torque, EMG and in muscle coordination compared to LL. The muscle synergy analysis is a muscle activation decomposition technique used in electromyogram signals. One common hypothesis in motor control is that each human movement is created by the combination of a small number of muscle synergies.
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