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Performance Comparison of Artificial Neural Network and Gaussian Mixture Model in Classifying Hand Motions by Using sEMG Signals

Paper ID Volume ID Publish Year Pages File Format Full-Text
5166 345 2013 13 PDF Available
Title
Performance Comparison of Artificial Neural Network and Gaussian Mixture Model in Classifying Hand Motions by Using sEMG Signals
Abstract

In this study, a home-made four channel sEMG amplifier circuit was designed for measu-ring of sEMG signals. The measured sEMG signals were recorded on to a computer with help of a DAQ board. The recorded sEMG signals were filtered first with a high-pass filter and afterwards a wavelet based filtering was applied to remove unwanted noises. Before applying of the wavelet based filtering, it was first determined which wavelet type, threshold selection rule and threshold would be suitable for the denoising process. As a second step, the recorded and denoised signals’ features were extracted. For classification of motions 8 time domain and 2 frequency domain features were used individually and in combinations. Lastly, seven different motions were classified and their classification performances were compared. In this study, classification rates of ANN and GMM classifiers were compared as regards features.

Keywords
hand motion classification; artificial neural network; gaussian mixture model
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Performance Comparison of Artificial Neural Network and Gaussian Mixture Model in Classifying Hand Motions by Using sEMG Signals
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Publisher
Database: Elsevier - ScienceDirect
Journal: Biocybernetics and Biomedical Engineering - Volume 33, Issue 1, 2013, Pages 33–45
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us