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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Veröffentlicht in:International journal of advanced robotic systems Jg. 17; H. 2; S. 172988142091701
Hauptverfasser: Wang, Zheng, Chen, Guoqi, Li, Weikun, Liu, Honghai, Wang, Wanliang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London, England SAGE Publications 01.03.2020
Sage Publications Ltd
SAGE Publishing
Schlagworte:
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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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1729-8806
1729-8814
1729-8814
DOI:10.1177/1729881420917016