Clustering analysis and recognition of the EMGs

Author

Ling, Huang ; Bo, You ; Lina, Zhou

Author_Institution

Coll. of Autom., Harbin Univ. of Sci. & Technol., Harbin, China

Volume

1

fYear

2011

fDate

25-28 July 2011

Firstpage

243

Lastpage

246

Abstract

In order to identify EMGs better, the separability and clustering is compared for different features of EMGs. Then the six channels EMGs from forearm are identified based on the features with better separability. The EMGs of 18 motions of a hand are collected, the time domain features and the frequency domain features of the motions are extracted, then the separability and clustering of the features are analysized, in the end the time domain features are sent to three classifiers, which are built for the thumb, forefinger and the other three fingers, for identification. The accuracy of distinguishing is 98%, 97% and 100% respectively.

Keywords

backpropagation; electromyography; feature extraction; gesture recognition; medical signal processing; neural nets; pattern clustering; source separation; time-frequency analysis; EMG recognition; backpropagation neural network; feature clustering; feature extraction; feature separability; forefinger; frequency domain features; gesture recognition; thumb; time domain features; Accuracy; Electromyography; Feature extraction; Fingers; Frequency domain analysis; Support vector machines; Time domain analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Information Processing (ICICIP), 2011 2nd International Conference on

Conference_Location

Harbin

Print_ISBN

978-1-4577-0813-8

Type

conf

DOI

10.1109/ICICIP.2011.6008240

Filename

6008240