EMG Feature Selection And Classification Using A Pbest-Guide Binary Particle Swarm Optimization

EMG Feature Selection And Classification Using A Pbest-Guide Binary Particle Swarm Optimization

Due to the increment in hand motion types, electromyography (EMG) features are increasingly required for accurate EMG signals classification. However, increasing in the number of EMG features not only degrades classification performance, but also increases the complexity of the classifier. Feature s...

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Bibliographic Details
Main Authors: Too, Jing Wei, Tee, Wei Hown, Abdullah, Abdul Rahim, Mohd Saad, Norhashimah
Format: Article
Language:en
Published: MDPI Multidisciplinary Digital Publishing Institute 2019
Online Access:http://eprints.utem.edu.my/id/eprint/24627/2/2019%20EMG%20FEATURE%20SELECTION%20AND%20CLASSIFICATION%20USING%20A%20PBEST-GUIDE%20BINARY%20PARTICLE%20SWARM%20OPTIMIZATION.PDF
http://eprints.utem.edu.my/id/eprint/24627/
https://www.mdpi.com/2079-3197/7/1/12/htm
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http://eprints.utem.edu.my/id/eprint/24627/2/2019%20EMG%20FEATURE%20SELECTION%20AND%20CLASSIFICATION%20USING%20A%20PBEST-GUIDE%20BINARY%20PARTICLE%20SWARM%20OPTIMIZATION.PDF
http://eprints.utem.edu.my/id/eprint/24627/
https://www.mdpi.com/2079-3197/7/1/12/htm

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