Processing and classification of Electromyography (EMG) signals is a common practice in prosthetic arm design for hand amputees. This study investigates how reliable classification results may be obtained from less muscle data, as is the case for the muscle loss in hand amputees. In order to increase classification performance, features from gyroscopic data, not previously used in studies in the literature, were investigated. Data was acquired from 10 normal subjects using the Myo armband for 7 hand gestures: fist, fingers spread, wave-in, wave-out, pronation, supination, and rest. Subjects repeated each gesture 30 times. EMG signals were preprocessed to extract features. Twenty (20) features were used in the feature matrix; 14 time domain and 6 frequency domain. Features were selected to determine the highest accuracy using the Support Vector Machine (SVM) and k-Nearest Neighbor (KNN) as classification algorithm. The Classification Learner application in Matlab® was used for classification. The highest accuracy using all EMG channels was 98.38 %. When the number of channels was reduced to 3, the accuracy was over 90 %. It is observed that gyroscopic features increase the performance when a small number of EMG channels is used.

肌电图（EMG）信号的处理和分类是手截肢者假肢设计的一种常见做法。这项研究调查了如何从较少的肌肉数据中获得可靠的分类结果，就像手截肢者的肌肉丢失一样。为了提高分类性能，研究了陀螺仪数据中以前没有在文献研究中使用过的特征。使用Myo臂章从10名正常受试者中获取了7种手势数据：拳头，手指张开，伸手，伸手，内旋，旋后和休息。受试者重复每个手势30次。肌电信号经过预处理以提取特征。特征矩阵中使用了二十（20）个特征。14个时域和6个频域。使用支持向量机（SVM）和k最近邻（KNN）作为分类算法，选择特征以确定最高精度。Matlab®中的分类学习器应用程序用于分类。使用所有EMG通道的最高准确度为98.38％。当通道数减少到3时，精度超过90％。可以观察到，当使用少量的EMG通道时，陀螺仪功能会提高性能。