Myoelectric signals are regarded as the control signal for prosthetic limbs. But, the main research challenge is reliable and repeatable movement detection using electromyography. In this study, the analysis of the muscle synergy pattern has been considered as a key idea to cope with this main challenge. 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 order to design such a system‚ the synergy patterns of the wrist muscles have been extracted and utilized to identify wrist movements. Also, different decision fusion algorithms were used to increase the reliability of the synergy pattern classification. The classification performance was evaluated while no data subject was enrolled. 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 average accuracy, obtained in an offline manner, was about 99.78 ± 0.45%. 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.

肌电信号被视为假肢的控制信号。但是，主要的研究挑战是使用肌电图进行可靠且可重复的运动检测。在这项研究中，对肌肉协同作用模式的分析已被认为是应对这一主要挑战的关键思想。这项研究的主要目的是提供一种分析工具，基于对肌肉协同作用模式的分析，通过肌电图（EMG）识别六种腕部运动。为了设计这样的系统，提取了腕部肌肉的协同作用模式，并将其用于识别腕部运动。另外，使用了不同的决策融合算法来提高协同模式分类的可靠性。在没有数据主体参与的情况下评估了分类性能。就获得的性能而言，使用多层感知器（MLP）神经网络作为融合算法被证明是最佳组合。通过离线方式获得的分类平均准确度约为99.78±0.45％。使用贝叶斯融合和贝叶斯模糊聚类（BFC）融合算法以离线方式获得的分类平均交叉验证准确性分别为99.33±0.80％和96.43±1.08％。