The problem of classifying individual finger movements of one hand is focused in this article. The input electromyography signal is processed and eight time-domain features are extracted for classifying hand gestures. The classified finger movements are thumb, middle, index, little, ring, hand close, thumb index, thumb ring, thumb little and thumb middle and the hand grasps are palmar class, spherical class, hook class, cylindrical class, tip class and lateral class. Four state-of-the-art classifiers namely feed forward artificial neural network, cascaded feed forward artificial neural network, deep learning neural network and support vector machine are selected for this work to classify the finger movements and hand grasps using the extracted time-domain features. The experimental results show that the artificial neural network classifier is stabilized at 6 epochs for finger movement dataset and at 4 epochs for hand grasps dataset with low mean square error. However, the support vector machine classifier attains the maximum accuracy of 97.3077% for finger movement dataset and 98.875% for hand grasp dataset which is significantly greater than feed forward artificial neural network, cascaded feed forward artificial neural network and deep learning neural network classifiers.

中文翻译：

---

使用肌电信号和时域特征对手势进行分类的模式识别网络的性能评估。

本文重点讨论对一只手的各个手指运动进行分类的问题。处理输入的肌电信号，并提取八个时域特征以对手势进行分类。分类的手指运动为拇指，中指，食指，小指，无名指，手闭合，拇指食指，拇指环，小拇指和拇指中指，手握的手法为手掌类，球形类，钩类，圆柱类，针尖类和外侧类。为此，选择了四个最先进的分类器，即前馈人工神经网络，级联前馈人工神经网络，深度学习神经网络和支持向量机，以使用提取的时域对手指运动和手部进行分类。特征。实验结果表明，人工神经网络分类器的手指运动数据集稳定在6个周期，手抓数据集稳定在4个周期，均方误差低。然而，支持向量机分类器对手指运动数据集的最大准确度为97.3077％，对于手握数据集的最大准确度为98.875％，远高于前馈人工神经网络，级联前馈人工神经网络和深度学习神经网络分类器。