Based on probabilistic neural network of human multi-channel Semg pattern recognition

Author

Changming Dai ; Chunmei Du ; Aiha Qi ; Jiwen Liu

Author_Institution

Educ. Dept., Hebei Univ. of Archit., Zhang Jia Kou, China

fYear

2012

fDate

29-31 Dec. 2012

Firstpage

1354

Lastpage

1356

Abstract

The phase space reconstruction theory combined with neural network topic of EMG signal analysis methods. Firstly, using the models of the fractal characteristics, calculatiion the two-dimensional spatial information entropy as reflected the muscles of the measuring unit of motion information. Then input the information entropy to the trained probability RBF to be classified Experimental results show that this method is an effective muscle signal pattern récognition method.

Keywords

electromyography; entropy; learning (artificial intelligence); medical signal processing; pattern recognition; probability; radial basis function networks; signal reconstruction; EMG signal analysis methods; fractal characteristics; human multichannel SEMG pattern recognition; motion information; muscle signal pattern recognition method; phase space reconstruction theory; probabilistic neural network; probability RBF; radial basis function network; surface electromyography; two-dimensional spatial information entropy; Probability RBF neural network; information entropy; semg;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Science and Network Technology (ICCSNT), 2012 2nd International Conference on

Conference_Location

Changchun

Print_ISBN

978-1-4673-2963-7

Type

conf

DOI

10.1109/ICCSNT.2012.6526172

Filename

6526172