Swarmed Discriminant Analysis for Multifunction Prosthesis Control

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dc.contributor.author Khushaba, RN
dc.contributor.author Al-Ani, A
dc.contributor.author Al-Jumaily, A
dc.date.accessioned 2012-10-12T03:33:44Z
dc.date.issued 2011-01
dc.identifier.citation International Journal of Engineering and Natural Sciences, 2011, 5 (1), pp. 27 - 34
dc.identifier.issn 2010-4006
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/18255
dc.description.abstract One of the approaches enabling people with amputated limbs to establish some sort of interface with the real world includes the utilization of the myoelectric signal (MES) from the remaining muscles of those limbs. The MES can be used as a control input to a multifunction prosthetic device. In this control scheme, known as the myoelectric control, a pattern recognition approach is usually utilized to discriminate between the MES signals that belong to different classes of the forearm movements. Since the MES is recorded using multiple 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 yet 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 estimating the weight of each feature. The new method, called PSOFLDA, is tested on real MES datasets and compared with other techniques to prove its superiority.
dc.format Ryan Stoker
dc.language English
dc.publisher World Academy of Science
dc.relation.hasversion Accepted manuscript version en_US
dc.title Swarmed Discriminant Analysis for Multifunction Prosthesis Control
dc.type Journal Article
dc.parent International Journal of Engineering and Natural Sciences
dc.journal.volume 1
dc.journal.volume 5
dc.journal.number 1 en_US
dc.publocation USA en_US
dc.publocation Sydney
dc.identifier.startpage 27 en_US
dc.identifier.endpage 34 en_US
dc.cauo.name FEIT.School of Elec, Mech and Mechatronic Systems en_US
dc.conference Verified OK en_US
dc.for 080109 Pattern Recognition and Data Mining
dc.for 080108 Neural, Evolutionary and Fuzzy Computation
dc.personcode 011083
dc.personcode 040052
dc.personcode 101188
dc.percentage 60 en_US
dc.classification.name Pattern Recognition and Data Mining en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Discriminant Analysis, Pattern Recognition, Signal Processing
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Elec, Mech and Mechatronic Systems
pubs.organisational-group /University of Technology Sydney/Strength - Health Technologies
utslib.copyright.status Open Access
utslib.copyright.date 2015-04-15 12:23:47.074767+10
pubs.consider-herdc true


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