Electromyography is a promising approach to the gesture recognition of humans if an efficient classifier with high accuracy is available. In this paper, we propose to utilize Extreme Value Machine (EVM) as a high-performance algorithm for the classification of EMG signals. We employ reflection coefficients obtained from an Autoregressive (AR) model to train a set of classifiers. Our experimental results indicate that EVM has better accuracy in comparison to the conventional classifiers approved in the literature based on K-Nearest Neighbors (KNN) and Support Vector Machine (SVM).

翻译：如果有一个精密高效的分类器,那么电传学是对人类的姿态识别的一种很有希望的方法。在本文中,我们提议利用极值机器作为高性能算法对环球磁感仪信号进行分类。我们使用自动递减模型获得的反射系数来培训一组分类器。我们的实验结果表明,与基于K-Nearest邻居和辅助矢量机(SVM)的文献中核准的常规分类器相比,EVM的准确性更高。