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Classification of Rock-Paper-Scissors using Electromyography and Multi-Layer Perceptron

Title
Classification of Rock-Paper-Scissors using Electromyography and Multi-Layer Perceptron
Author
최영진
Keywords
Electromyography(EMG); multi-layer perceptron(MLP); muscle activation; posture classification
Issue Date
2017-06
Publisher
IEEE
Citation
2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Page. 406-407
Abstract
The paper presents a method to classify electromyographic (EMG) signals according to the postures of rock-paper-scissors by using multi-layer perceptrons (MLPs). The EMGs are first applied to He-Zajac-Levine bilinear activation model and then the output of model is utilized to be inputs of the MLPs. Cross validation method is used to evaluate the classification performance of MLPs and its outcome also shows that accuracy of the proposed method is over 97%. © 2017 IEEE.
URI
https://ieeexplore.ieee.org/document/7992763/https://repository.hanyang.ac.kr/handle/20.500.11754/103360
ISBN
978-1-5090-3056-9
DOI
10.1109/URAI.2017.7992763
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ELECTRICAL ENGINEERING(전자공학부) > Articles
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