Surface electromyography (sEMG) feature extraction based on Daubechies wavelets

I. Elamvazuthi; G. A. Ling; K. A.R.Ku Nurhanim; P. Vasant; S. Parasuraman

doi:10.1109/ICIEA.2013.6566603

Surface electromyography (sEMG) feature extraction based on Daubechies wavelets

I. Elamvazuthi, G. A. Ling, K. A.R.Ku Nurhanim, P. Vasant, S. Parasuraman

Malaysia School of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Other

22 Citations (Scopus)

Abstract

Wavelet transform feature extraction has become one of the most powerful techniques to improve the classification accuracy. In this paper, we are investigating the multi-level Daubechies wavelet reconstruction parameters. The EMG signal after performing the Daubechies wavelet was further processed by using one of the most successful features which is MAV. RES index statistical measurement was used to evaluate the class reparability of the features. The optimal results are obtained by using the seventh order of Daubechies with the level 1 and level 2 details components after performing wavelet reconstruction.

Original language	English
Title of host publication	Proceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications, ICIEA 2013
Pages	1492-1495
Number of pages	4
DOIs	https://doi.org/10.1109/ICIEA.2013.6566603
Publication status	Published - 2013
Event	IEEE Conference on Industrial Electronics and Applications 2013 - Melbourne, Australia Duration: 19 Jun 2013 → 21 Jun 2013 Conference number: 8th http://ieeeiciea.org/2013/index.html http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001289 (Proceedings)

Conference

Conference	IEEE Conference on Industrial Electronics and Applications 2013
Abbreviated title	ICIEA 2013
Country/Territory	Australia
City	Melbourne
Period	19/06/13 → 21/06/13
Internet address	http://ieeeiciea.org/2013/index.html http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1001289 (Proceedings)

Keywords

Daubechies wavelet
Electromyography signal
EMG
feature extraction

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10.1109/ICIEA.2013.6566603

Cite this

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title = "Surface electromyography (sEMG) feature extraction based on Daubechies wavelets",

abstract = "Wavelet transform feature extraction has become one of the most powerful techniques to improve the classification accuracy. In this paper, we are investigating the multi-level Daubechies wavelet reconstruction parameters. The EMG signal after performing the Daubechies wavelet was further processed by using one of the most successful features which is MAV. RES index statistical measurement was used to evaluate the class reparability of the features. The optimal results are obtained by using the seventh order of Daubechies with the level 1 and level 2 details components after performing wavelet reconstruction.",

keywords = "Daubechies wavelet, Electromyography signal, EMG, feature extraction",

author = "I. Elamvazuthi and Ling, {G. A.} and Nurhanim, {K. A.R.Ku} and P. Vasant and S. Parasuraman",

year = "2013",

doi = "10.1109/ICIEA.2013.6566603",

language = "English",

isbn = "9781467363211",

pages = "1492--1495",

booktitle = "Proceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications, ICIEA 2013",

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}

Elamvazuthi, I, Ling, GA, Nurhanim, KARK, Vasant, P & Parasuraman, S 2013, Surface electromyography (sEMG) feature extraction based on Daubechies wavelets. in Proceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications, ICIEA 2013. pp. 1492-1495, IEEE Conference on Industrial Electronics and Applications 2013, Melbourne, Victoria, Australia, 19/06/13. https://doi.org/10.1109/ICIEA.2013.6566603

TY - GEN

T1 - Surface electromyography (sEMG) feature extraction based on Daubechies wavelets

AU - Elamvazuthi, I.

AU - Ling, G. A.

AU - Nurhanim, K. A.R.Ku

AU - Vasant, P.

AU - Parasuraman, S.

N1 - Conference code: 8th

PY - 2013

Y1 - 2013

N2 - Wavelet transform feature extraction has become one of the most powerful techniques to improve the classification accuracy. In this paper, we are investigating the multi-level Daubechies wavelet reconstruction parameters. The EMG signal after performing the Daubechies wavelet was further processed by using one of the most successful features which is MAV. RES index statistical measurement was used to evaluate the class reparability of the features. The optimal results are obtained by using the seventh order of Daubechies with the level 1 and level 2 details components after performing wavelet reconstruction.

AB - Wavelet transform feature extraction has become one of the most powerful techniques to improve the classification accuracy. In this paper, we are investigating the multi-level Daubechies wavelet reconstruction parameters. The EMG signal after performing the Daubechies wavelet was further processed by using one of the most successful features which is MAV. RES index statistical measurement was used to evaluate the class reparability of the features. The optimal results are obtained by using the seventh order of Daubechies with the level 1 and level 2 details components after performing wavelet reconstruction.

KW - Daubechies wavelet

KW - Electromyography signal

KW - EMG

KW - feature extraction

UR - http://www.scopus.com/inward/record.url?scp=84881428038&partnerID=8YFLogxK

U2 - 10.1109/ICIEA.2013.6566603

DO - 10.1109/ICIEA.2013.6566603

M3 - Conference Paper

AN - SCOPUS:84881428038

SN - 9781467363211

SP - 1492

EP - 1495

BT - Proceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications, ICIEA 2013

T2 - IEEE Conference on Industrial Electronics and Applications 2013

Y2 - 19 June 2013 through 21 June 2013

ER -

Surface electromyography (sEMG) feature extraction based on Daubechies wavelets

Abstract

Conference

Keywords

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