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Feature-channel subset selection for optimising myoelectric human-machine interface design

Oskoei, Mohammadreza Asghari and Hu, Huosheng and Gan, John Q (2013) 'Feature-channel subset selection for optimising myoelectric human-machine interface design.' International Journal of Biomechatronics and Biomedical Robotics, 2 (2/3/4). pp. 195-208. ISSN 1757-6792

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Abstract

This paper proposes a feature-channel subset selection method for obtaining an optimal subset of features and channels of myoelectric human-machine interface applied to classify upper limb’s motions using multi-channel myoelectric signals. It employs a multi-objective genetic algorithm as a search strategy and either data separability index or classification rate as an objective function. A wide range of features in time, frequency, and time-scale domains are examined, and a modification that reduces the bias of cardinality in the separability index is evaluated. The proposed method produces a compact subset of features and channels, which results in high accuracy by linear classifiers without a need of preliminary tailor-made adjustments.

Item Type: Article
Uncontrolled Keywords: myoelectric HMI, human–machine interface, interface design, feature subset selection, multi–objective genetic algorithms, MOGA, Davies–Bouldin index, DBI, upper limb motions, linear classifiers
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Clare Chatfield
Date Deposited: 09 Sep 2014 13:29
Last Modified: 22 Jul 2015 13:53
URI: http://repository.essex.ac.uk/id/eprint/9227

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