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Neural Computing and Applications
Published in: Neural Computing and Applications 10/2020

15-03-2019 | Original Article

Surface EMG hand gesture recognition system based on PCA and GRNN

Authors: Jinxian Qi, Guozhang Jiang, Gongfa Li, Ying Sun, Bo Tao

Published in: Neural Computing and Applications | Issue 10/2020

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Abstract

The principal component analysis method and GRNN neural network are used to construct the gesture recognition system, so as to reduce the redundant information of EMG signals, reduce the signal dimension, improve the recognition efficiency and accuracy, and enhance the feasibility of real-time recognition. Using the means of extracting key information of human motion, the specific action mode is identified. In this paper, nine static gestures are taken as samples, and the surface EMG signal of the arm is collected by the electromyography instrument to extract four kinds of characteristics of the signal. After dimension reduction and neural network learning, the overall recognition rate of the system reached 95.1%, and the average recognition time was 0.19 s.
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Metadata
Title
Surface EMG hand gesture recognition system based on PCA and GRNN
Authors
Jinxian Qi
Guozhang Jiang
Gongfa Li
Ying Sun
Bo Tao
Publication date
15-03-2019
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 10/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04142-8

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