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Impact of feature extraction techniques on classification accuracy for EMG based ankle joint movements

EMG based control becomes the core of the pros-theses, orthoses and rehabilitation devices in the recent research. Though the difficulties of using EMG as a control signal due to the complexity nature of this signal, the researchers employed the pattern recognition technique to overcome this problem...

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Main Authors: Al-Quraishi, Maged Saleh Saeed, Ishak, Asnor Juraiza, Ahmad, Siti Anom, Hassan, Mohd Khair
Format: Conference or Workshop Item
Language:English
Published: IEEE 2015
Online Access:http://psasir.upm.edu.my/id/eprint/56101/1/Impact%20of%20feature%20extraction%20techniques%20on%20classification%20accuracy%20for%20EMG%20based%20ankle%20joint%20movements.pdf
http://psasir.upm.edu.my/id/eprint/56101/
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spelling my.upm.eprints.561012017-07-03T09:36:57Z http://psasir.upm.edu.my/id/eprint/56101/ Impact of feature extraction techniques on classification accuracy for EMG based ankle joint movements Al-Quraishi, Maged Saleh Saeed Ishak, Asnor Juraiza Ahmad, Siti Anom Hassan, Mohd Khair EMG based control becomes the core of the pros-theses, orthoses and rehabilitation devices in the recent research. Though the difficulties of using EMG as a control signal due to the complexity nature of this signal, the researchers employed the pattern recognition technique to overcome this problem. The EMG pattern recognition mainly consists of four stages; signal detection and preprocessing feature extraction, dimensionality reduction and classification. However, the success of any pattern recognition technique depends on the feature extraction and dimensionality reduction stages. In this paper time domain (TD) with 6th order auto regressive (AR) coefficients features and three techniques of dimensionality reduction; principal component analysis (PCA), uncorrelated linear discriminant analysis (ULDA) and fuzzy neighborhood preserving analysis with QR decomposition (FNPA-QR) were demonstrated. The EMG data were recorded from the below knee muscles of ten intact-subjects. Four ankle joint movements are classified using three classifiers; LDA, k-NN and MLP. The results show the superiority of TD&6th AR with FNPA-QR and k-NN combination with (96.20% ± 4.1) accuracy. IEEE 2015 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/56101/1/Impact%20of%20feature%20extraction%20techniques%20on%20classification%20accuracy%20for%20EMG%20based%20ankle%20joint%20movements.pdf Al-Quraishi, Maged Saleh Saeed and Ishak, Asnor Juraiza and Ahmad, Siti Anom and Hassan, Mohd Khair (2015) Impact of feature extraction techniques on classification accuracy for EMG based ankle joint movements. In: 10th Asian Control Conference (ASCC 2015), 31 May-3 June 2015, Kota Kinabalu, Sabah. . 10.1109/ASCC.2015.7244844
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description EMG based control becomes the core of the pros-theses, orthoses and rehabilitation devices in the recent research. Though the difficulties of using EMG as a control signal due to the complexity nature of this signal, the researchers employed the pattern recognition technique to overcome this problem. The EMG pattern recognition mainly consists of four stages; signal detection and preprocessing feature extraction, dimensionality reduction and classification. However, the success of any pattern recognition technique depends on the feature extraction and dimensionality reduction stages. In this paper time domain (TD) with 6th order auto regressive (AR) coefficients features and three techniques of dimensionality reduction; principal component analysis (PCA), uncorrelated linear discriminant analysis (ULDA) and fuzzy neighborhood preserving analysis with QR decomposition (FNPA-QR) were demonstrated. The EMG data were recorded from the below knee muscles of ten intact-subjects. Four ankle joint movements are classified using three classifiers; LDA, k-NN and MLP. The results show the superiority of TD&6th AR with FNPA-QR and k-NN combination with (96.20% ± 4.1) accuracy.
format Conference or Workshop Item
author Al-Quraishi, Maged Saleh Saeed
Ishak, Asnor Juraiza
Ahmad, Siti Anom
Hassan, Mohd Khair
spellingShingle Al-Quraishi, Maged Saleh Saeed
Ishak, Asnor Juraiza
Ahmad, Siti Anom
Hassan, Mohd Khair
Impact of feature extraction techniques on classification accuracy for EMG based ankle joint movements
author_facet Al-Quraishi, Maged Saleh Saeed
Ishak, Asnor Juraiza
Ahmad, Siti Anom
Hassan, Mohd Khair
author_sort Al-Quraishi, Maged Saleh Saeed
title Impact of feature extraction techniques on classification accuracy for EMG based ankle joint movements
title_short Impact of feature extraction techniques on classification accuracy for EMG based ankle joint movements
title_full Impact of feature extraction techniques on classification accuracy for EMG based ankle joint movements
title_fullStr Impact of feature extraction techniques on classification accuracy for EMG based ankle joint movements
title_full_unstemmed Impact of feature extraction techniques on classification accuracy for EMG based ankle joint movements
title_sort impact of feature extraction techniques on classification accuracy for emg based ankle joint movements
publisher IEEE
publishDate 2015
url http://psasir.upm.edu.my/id/eprint/56101/1/Impact%20of%20feature%20extraction%20techniques%20on%20classification%20accuracy%20for%20EMG%20based%20ankle%20joint%20movements.pdf
http://psasir.upm.edu.my/id/eprint/56101/
_version_ 1643836090864369664
score 13.211869

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