Multichannel optimization for electromyogram signals with complex features in a decomposition-based multi-objective evolution framework with adaptive angle selection

Intelligent manufacturing is a focus of current manufacturing research, and, in combination with the Internet, it enables accurate real-time control of intelligent equipment. Highly accurate real-time prosthesis control has very important applications in therapeutics, intelligent prosthesis, and oth...

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Vydáno v:	International journal of advanced robotic systems Ročník 17; číslo 2; s. 172988142091701
Hlavní autoři:	Wang, Zheng, Chen, Guoqi, Li, Weikun, Liu, Honghai, Wang, Wanliang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London, England SAGE Publications 01.03.2020 Sage Publications Ltd SAGE Publishing
Témata:	Ant colony optimization Channels Complexity Control equipment Decomposition Evolutionary algorithms Evolutionary computation Genetic algorithms Intelligent manufacturing systems Multichannel communication Multiple objective analysis Prostheses Real time Recognition Signal classification Support vector machines surface electromyogram signal multi-objective evolution Multichannel optimization adaptive angle selection support vector machine classifier
ISSN:	1729-8806, 1729-8814, 1729-8814
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Abstract	Intelligent manufacturing is a focus of current manufacturing research, and, in combination with the Internet, it enables accurate real-time control of intelligent equipment. Highly accurate real-time prosthesis control has very important applications in therapeutics, intelligent prosthesis, and other fields. However, the applicability of the current electromyogram signal recognition method is not strong because of multiple factors. These include considering one objective (correctness) only and the inability to consider differences of recognition accuracy between actions, to recognize the number of channels, or to recognize computational complexity. In this article, we propose a multi-objective evolutionary algorithm based on a decomposition-based multi-objective differential evolution framework to construct a multi-objective model for electromyogram signals with multiple features and channels. Such channels and features are balanced and selected by using a support vector machine as an electromyogram signal classifier. Results of substantial experiment analyses indicate that the multi-objective electromyogram signal recognition method is superior to the single-objective ant colony algorithm and that the decomposition-based multiobjective evolutionary algorithms with Angle-based updating and global margin ranking is better than the decomposition-based multi-objective evolutionary algorithm and decomposition-based multiobjective evolutionary algorithms with angle-based updating strategy in handling multi-objective models for electromyogram signals.
AbstractList	Intelligent manufacturing is a focus of current manufacturing research, and, in combination with the Internet, it enables accurate real-time control of intelligent equipment. Highly accurate real-time prosthesis control has very important applications in therapeutics, intelligent prosthesis, and other fields. However, the applicability of the current electromyogram signal recognition method is not strong because of multiple factors. These include considering one objective (correctness) only and the inability to consider differences of recognition accuracy between actions, to recognize the number of channels, or to recognize computational complexity. In this article, we propose a multi-objective evolutionary algorithm based on a decomposition-based multi-objective differential evolution framework to construct a multi-objective model for electromyogram signals with multiple features and channels. Such channels and features are balanced and selected by using a support vector machine as an electromyogram signal classifier. Results of substantial experiment analyses indicate that the multi-objective electromyogram signal recognition method is superior to the single-objective ant colony algorithm and that the decomposition-based multiobjective evolutionary algorithms with Angle-based updating and global margin ranking is better than the decomposition-based multi-objective evolutionary algorithm and decomposition-based multiobjective evolutionary algorithms with angle-based updating strategy in handling multi-objective models for electromyogram signals.
Author	Liu, Honghai Chen, Guoqi Li, Weikun Wang, Zheng Wang, Wanliang
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Cites_doi	10.1016/j.medengphy.2014.04.003 10.1016/j.ins.2016.08.043 10.1080/17517575.2012.683812 10.1007/s00521-017-2884-0 10.1109/TEVC.2015.2457616 10.1109/4235.797969 10.1177/1729881419862164 10.1162/106365600568202 10.1109/TCYB.2015.2403131 10.1016/j.asoc.2017.03.041 10.1109/TEVC.2011.2166159 10.1007/s10845-015-1042-8 10.1016/S0272-6963(02)00108-0 10.1109/TEVC.2008.925798 10.1016/j.jclepro.2019.06.078 10.1162/EVCO_a_00109 10.1007/s40684-016-0015-5 10.1016/j.energy.2017.09.103 10.1109/TEVC.2007.892759
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Keywords	surface electromyogram signal multi-objective evolution Multichannel optimization adaptive angle selection support vector machine classifier
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SubjectTerms	Ant colony optimization Channels Complexity Control equipment Decomposition Evolutionary algorithms Evolutionary computation Genetic algorithms Intelligent manufacturing systems Multichannel communication Multiple objective analysis Prostheses Real time Recognition Signal classification Support vector machines
Title	Multichannel optimization for electromyogram signals with complex features in a decomposition-based multi-objective evolution framework with adaptive angle selection
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