![]() ![]() The purpose of the present study was to investigate the muscle coordination among different shoulder muscles underlying basic shoulder movements based on muscle synergy. 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. Muscle synergy can be utilized as an index to determine muscle coordination. 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. MSs are fundamentally invariant patterns of activation across multiple muscles that are linearly combined to produce complex muscle activation patterns. 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. The muscle synergy is a guiding concept in motor control research that relies on the general notion of muscles working together towards task performance. 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. Muscle synergies were extracted applying a non-negative matrix factorization algorithm, and relative co-activations across muscles and the temporal recruitment pattern were identified by muscle. The algorithm of non-negative matrix factorization is used in extracting the muscle synergy and reconstructing signals. In this study, simulated muscle activation signals are produced by the combination of wavelets, similar to the real signals. By including both muscle activation and internal net joint moment data in our analysis we identified muscle synergies associated with individual moments. 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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