A training strategy to reduce classification degradation due to electrode displacements in pattern recognition based myoelectric control
A training strategy to reduce classification degradation due to electrode displacements in pattern recognition based myoelectric control
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DOI：
10.1016/j.bspc.2007.11.005
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发表时间：
2008-04-01
影响因子：
5.1
通讯作者：
Hudgins, Bernard
中科院分区：
文献类型：
--
作者：
Hargrove, Levi;Englehart, Kevin;Hudgins, Bernard
基于模式识别的肌电控制系统依赖于检测给定电极位置处的可重复模式。这项工作描述了一个实验，以确定电极位移对模式分类精度的影响，和分类器的训练策略，以适应这种退化。结果表明，电极位移对分类精度产生不利影响，但训练系统识别合理的位移位置可以减轻这种影响。此外，时域和自回归特征的组合似乎产生最佳的分类精度，并且受电极位移的影响最小。(C)2007年爱思唯尔有限公司保留所有战斗。
Pattern recognition based myoclectric control systems rely on detecting repeatable patterns at given electrode locations. This work describes an experiment to determine the effect of electrode displacements on pattern classification accuracy, and a classifier training strategy to accommodate this degradation. The results show that electrode displacements adversely affect classification accuracy, but training the system to recognize plausible displacement locations mitigates the effect. Furthermore, a combination of time-domain and autoregressive features appears to yield the best classification accuracy and is least affected by electrode displacements. (C) 2007 Elsevier Ltd. All fights reserved.