GA-based feature subset selection for myoelectric classification

被引：64

作者：

Oskoei, Mohammadreza Asghari ^{[1
]}

Hu, Huosheng ^{[1
]}

机构：

[1] Univ Essex, Dept Comp Sci, Wivenhoe Pk, Colchester CO4 3SQ, Essex, England

来源：

2006 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND BIOMIMETICS, VOLS 1-3 | 2006年

关键词：

Feature Subset Selection; EMG / Myoelectric signal classification; Genetic Algorithm; Class Separability index;

D O I：

10.1109/ROBIO.2006.340145

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents an ongoing investigation to select optimal subset of features from set of well-known myoelectric signals (MES) features in time and frequency domains. Four channel of myoelectric signal from upper limb muscles are used in this paper to classify six distinctive activities. Cascaded genetic algorithm (GA) has been adopted as the search strategy in feature subset selection. Davies-Bouldin index (DBI) and Fishers linear discriminant index (FLDI) are employed as the filter objective functions and linear discriminant analysis (LDA) has been used as the wrapper objective function. Results prove more accurate and reliable classification for the elite subset of features applying to artificial neural networks as the classifier.

引用

页码：1465 / +

页数：2