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dc.contributor.author최영진-
dc.date.accessioned2019-05-03T05:24:34Z-
dc.date.available2019-05-03T05:24:34Z-
dc.date.issued2017-06-
dc.identifier.citation2017 14th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI), Page. 406-407en_US
dc.identifier.isbn978-1-5090-3056-9-
dc.identifier.urihttps://ieeexplore.ieee.org/document/7992763/-
dc.identifier.urihttps://repository.hanyang.ac.kr/handle/20.500.11754/103360-
dc.description.abstractThe 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.en_US
dc.description.sponsorshipThis work was supported by the Convergence Technology Development Program for Bionic Arm through the National Research Foundation of Korea funded by the Ministry of Science, ICT & Future Planning (NRF-2015M3C1B2052811), Republic of Korea.en_US
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.subjectElectromyography(EMG)en_US
dc.subjectmulti-layer perceptron(MLP)en_US
dc.subjectmuscle activationen_US
dc.subjectposture classificationen_US
dc.titleClassification of Rock-Paper-Scissors using Electromyography and Multi-Layer Perceptronen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/URAI.2017.7992763-
dc.relation.page406-407-
dc.contributor.googleauthorGang, Taeho-
dc.contributor.googleauthorCho, Younggil-
dc.contributor.googleauthorChoi, Youngjin-
dc.sector.campusE-
dc.sector.daehakCOLLEGE OF ENGINEERING SCIENCES[E]-
dc.sector.departmentDIVISION OF ELECTRICAL ENGINEERING-
dc.identifier.pidcyj-
Appears in Collections:
COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > ELECTRICAL ENGINEERING(전자공학부) > Articles
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