EMG based classification of basic hand movements based on time-frequency features

被引:0
|
作者
Sapsanis, Christos [1 ]
Georgoulas, George [2 ]
Tzes, Anthony [1 ]
机构
[1] Univ Patras, Dept Elect & Comp Engn, GR-26110 Patras, Greece
[2] Technol Educ Inst Epirus, KIC Lab, Dept Informat & Telecommun, Epirus GR-47100, Greece
关键词
Biomedical signal analysis; Empirical Mode Decomposition; RELIEF feature selection; Principal Component analysis; pattern classification; electromyography;
D O I
暂无
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 proposes an integrated approach for the identification of daily hand movements with a view to control prosthetic members. The raw EMG signal is decomposed into Intrinsic Mode Functions (IMFs) with the use of Empirical Mode Decomposition (EMD). A number of features are extracted in time and in frequency domain. Two different dimentionality methods are tested, namely the Principal Component Analysis (PCA) technique and the RELIEF feature selection algorithm. The outputs of the dimensionality reduction stage are then fed to a linear classifier to perform the detection task. The approach was tested on a group of young individuals and the results appear promising.
引用
收藏
页码:716 / 722
页数:7
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