8/18/2023 0 Comments Define synergy pattern![]() While using the synergy-based analysis, the muscle interaction dynamics can be studied. In this manner, the muscle coactivation dynamics, characterizing the movement pattern, cannot be revealed. In fact, according to a specificity approach, activation dynamics of each involved muscle is analyzed distinctly. The authors believe that most of these challenges are raised because of specificity movement detection approaches used in the aforementioned researches. The other challenge is detecting the movements with multi DoF, and some research works were focused on detecting the movement with only one DoF. But some impediments such as number of the selected features, the computational cost of the required, and the minimum number of the data required for training the classifier can limit their practical applications. The research results of the past decades have shown that the pattern recognition-based methodology has promising results. , nine types of wrist movements were detected based on packet wavelet analysis using MLP and self-organizing feature map (SOFM) neural networks with an accuracy of 97%. They used the feed-forward multi-layer perceptron (MLP) and four TD features. investigated the recognition of hand and finger movements individually and reported the different accuracies for able-bodied subjects and amputated ones. recognized six predetermined tasks of muscle activation patterns using TD features and linear discriminant analysis classifiers in stroke subjects. Also, it depends on the type of subject (able-bodied or amputee). The classification accuracy in the classification model depends on the type of classification algorithm (i.e., supervised or unsupervised) and the features which are selected. The computational cost of TD features is less than the computational of FD features, yet yield comparable classification accuracy. So, in these researches, the first step is windowing the EMG signal and extracting a set of important features from time-windowed signal in the time domain (TD) and in the frequency domain (FD). Pattern recognition algorithms are applied to classify the EMG activity patterns in multiple muscles. Hence, the estimation algorithms, pattern recognition and regression methods, and their combination are among the topics of interest to researchers. Many researchers are engaged in improving the performance of such recognition algorithms in order to improve the efficiency of prostheses. One class of the known control approaches are based on recognizing the pattern of EMG signals elicited from the residual healthy muscles. Extensive research has been done in order to control various functions and increase the efficiency of prostheses (i.e., several degrees-of-freedom (DoF). Due to the useful application of EMG signal in clinical diagnoses, and biomedical applications as well as rehabilitation, they are considered as one of the best resources of controlling (i.e., prostheses, robots, and human-computer interfaces), recognition of intended limb movements. The EMG signal recorded from each surface electrode is the total potential of the motor units in the region where the electrode is positioned. The electromyogram (EMG) signal represents the electrical potentials generated in the muscles during muscle contraction, which shows the important neuromuscular information. While the classification average cross-validation accuracy, obtained in an offline manner, using Bayesian fusion, and Bayesian fuzzy clustering (BFC) fusion algorithm were 99.33 ± 0.80% and 96.43 ± 1.08%, respectively. The classification average accuracy, obtained in an offline manner, was about 99.78 ± 0.45%. In terms of the achieved performance, using a multi-layer perceptron (MLP) neural network as the fusion algorithm turned out to be the best combination. The classification performance was evaluated while no data subject was enrolled. Also, different decision fusion algorithms were used to increase the reliability of the synergy pattern classification. ![]() In order to design such a system‚ the synergy patterns of the wrist muscles have been extracted and utilized to identify wrist movements. The main objective of this research was to provide an analytical tool to recognize six wrist movements through electromyography (EMG) based on analysis of the muscle synergy patterns. In this study, the analysis of the muscle synergy pattern has been considered as a key idea to cope with this main challenge. But, the main research challenge is reliable and repeatable movement detection using electromyography. Myoelectric signals are regarded as the control signal for prosthetic limbs.
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