Phinyomark, A and Hu, H and Phukpattaranont, P and Limsakul, C (2012) Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification. Measurement Science Review, 12 (3). pp. 82-89. DOI https://doi.org/10.2478/v10048-012-0015-8
Phinyomark, A and Hu, H and Phukpattaranont, P and Limsakul, C (2012) Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification. Measurement Science Review, 12 (3). pp. 82-89. DOI https://doi.org/10.2478/v10048-012-0015-8
Phinyomark, A and Hu, H and Phukpattaranont, P and Limsakul, C (2012) Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification. Measurement Science Review, 12 (3). pp. 82-89. DOI https://doi.org/10.2478/v10048-012-0015-8
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.
Item Type: | Article |
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Uncontrolled Keywords: | Electromyography signal; EMG; uncorrelated LDA; orthogonal LDA; orthogonal fuzzy neighborhood discriminant analysis; kernel discriminant analysis; QR decomposition; feature extraction; feature projection |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 10 Sep 2014 09:52 |
Last Modified: | 30 Oct 2024 19:52 |
URI: | http://repository.essex.ac.uk/id/eprint/9194 |
Available files
Filename: v10048-012-0015-8.pdf
Licence: Creative Commons: Attribution 3.0