<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>The classification of upper-limb movements based on surface electromyography (EMG) signals is an important issue in the control of assistive devices and rehabilitation systems. Increasing the number of EMG channels and features in order to increase the number of control commands can yield a high dimensional feature vector. To cope with the accuracy and computation problems associated with high dimensionality, it is commonplace to apply a processing step that transforms the data to a space of significantly lower dimensions with only a limited loss of useful information. Linear discriminant analysis (LDA) has been successfully applied as an EMG feature projection method. Recently, a number of extended LDA-based algorithms have been proposed, which are more competitive in terms of both classification accuracy and computational costs/times with classical LDA. This paper presents the findings of a comparative study of classical LDA and five extended LDA methods. From a quantitative comparison based on seven multi-feature sets, three extended LDA-based algorithms, consisting of uncorrelated LDA, orthogonal LDA and orthogonal fuzzy neighborhood discriminant analysis, produce better class separability when compared with a baseline system (without feature projection), principle component analysis (PCA), and classical LDA. Based on a 7-dimension time domain and time-scale feature vectors, these methods achieved respectively 95.2% and 93.2% classification accuracy by using a linear discriminant classifier.</p></abstract>

The classification of upper-limb movements based on surface electromyography (EMG) signals is an important issue in the control of assistive devices and rehabilitation systems. Increasing the number of EMG channels and features in order to increase the number of control commands can yield a high dimensional feature vector. To cope with the accuracy and computation problems associated with high dimensionality, it is commonplace to apply a processing step that transforms the data to a space of significantly lower dimensions with only a limited loss of useful information. Linear discriminant analysis (LDA) has been successfully applied as an EMG feature projection method. Recently, a number of extended LDA-based algorithms have been proposed, which are more competitive in terms of both classification accuracy and computational costs/times with classical LDA. This paper presents the findings of a comparative study of classical LDA and five extended LDA methods. From a quantitative comparison based on seven multi-feature sets, three extended LDA-based algorithms, consisting of uncorrelated LDA, orthogonal LDA and orthogonal fuzzy neighborhood discriminant analysis, produce better class separability when compared with a baseline system (without feature projection), principle component analysis (PCA), and classical LDA. Based on a 7-dimension time domain and time-scale feature vectors, these methods achieved respectively 95.2% and 93.2% classification accuracy by using a linear discriminant classifier.

Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification

Department of Electrical Engineering, Faculty of Engineering, Prince of Songkla University, 15 Kanjanavanich Road, Kho Hong, Hat Yai, Songkhla 90112 Thailand

School of Computer Science & Electronic Engineering, University of Essex, Colchester CO4 3SQ U.K.

Measurement Science Review

The classification of upper-limb movements based on surface electromyography (EMG) signals is an important issue in the control of assistive devices and...

Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification

Publié en ligne: 28 mai 2012

Pages: 82 - 89

DOI: https://doi.org/10.2478/v10048-012-0015-8

Mots clés
Electromyography signal, EMG, uncorrelated LDA, orthogonal LDA, orthogonal fuzzy neighborhood discriminant analysis, kernel discriminant analysis, QR decomposition, feature extraction, feature projection

This content is open access.

Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification

Publié en ligne: 28 mai 2012

Pages: 82 - 89

DOI: https://doi.org/10.2478/v10048-012-0015-8

Mots clésElectromyography signal, EMG, uncorrelated LDA, orthogonal LDA, orthogonal fuzzy neighborhood discriminant analysis, kernel discriminant analysis, QR decomposition, feature extraction, feature projection

This content is open access.

Mots clés
Electromyography signal, EMG, uncorrelated LDA, orthogonal LDA, orthogonal fuzzy neighborhood discriminant analysis, kernel discriminant analysis, QR decomposition, feature extraction, feature projection