Channel and feature selection for a surface electromyographic pattern recognition task
Abstract
The objective of this research is to select a reduced group of surface electromyographic (sEMG) channels and signal-features that is able to provide an accurate classification rate in a myoelectric control system for any user. To that end, the location of 32 sEMG electrodes placed around-along the forearm and 86 signal-features are evaluated simultaneously in a static-hand gesture classification task (14 different gestures). A novel multivariate...
Paper Details
Title
Channel and feature selection for a surface electromyographic pattern recognition task
Published Date
Sep 1, 2014
Volume
41
Issue
11
Pages
5190 - 5200
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