EMG signal classification using conic section function neural networks
EMG signal classification using conic section function neural networks
复制标题
使用圆锥曲线函数神经网络对 EMG 信号进行分类
DOI：
10.1109/ijcnn.1999.836251
复制
发表时间：
1999
期刊：
影响因子：
--
通讯作者：
H. Seker
中科院分区：
文献类型：
--
作者：
Lale Özyilmaz;T. Yıldırım;H. Seker
这项工作的目的是利用一种新的神经网络结构对肌电信号进行分类，以控制多功能假肢。这些假体的控制可以通过从一对表面电极获取的肌电信号来实现。这种情况已被证明是专门用于以上肘部截肢者。分离不同肌肉收缩特征的能力依赖于肌电信号信息。因此，对这些信号的分类进行了研究。本文提出的神经网络算法可以使用户更好更快地学习。
The aim of this work is to classify EMG signals using a new neural network architecture to control multifunction prostheses. The control of these prostheses can be made using myoelectric signals taken from a single pair of surface electrodes. This case has been demonstrated specifically for use by above elbow amputees. The ability to separate different muscle contraction characters depends on myoelectric signal information. Therefore, the classification of these signals is investigated. The proposed neural network algorithm here makes the user learn better and faster.