Swarm based fuzzy discriminant analysis for multifunction prosthesis control

Authors:
Rami N. Khushaba;Ahmed Al-Ani;Adel Al-Jumaily
Affiliations:
Faculty of Engineering, University of Technology, Sydney, Australia;Faculty of Engineering, University of Technology, Sydney, Australia;Faculty of Engineering, University of Technology, Sydney, Australia
Venue:
ANNPR'10 Proceedings of the 4th IAPR TC3 conference on Artificial Neural Networks in Pattern Recognition
Year:
2010

Citing 3
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Abstract

In order to interface the amputee’s with the real world, the myoelectric signal (MES) from human muscles is usually utilized within a pattern recognition scheme as an input to the controller of a prosthetic device. Since the MES is recorded using multi channels, the feature vector size can become very large. In order to reduce the computational cost and enhance the generalization capability of the classifier, a dimensionality reduction method is needed to identify an informative moderate size feature set. This paper proposes a new fuzzy version of the well known Fisher’s Linear Discriminant Analysis (LDA) feature projection technique. Furthermore, based on the fact that certain muscles might contribute more to the discrimination process, a novel feature weighting scheme is also presented by employing Particle Swarm Optimization (PSO) for the weights calculation. The new method, called PSOFLDA, is tested on real MES datasets and compared with other techniques to prove its superiority.