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

Gang, Taeho; Cho, Younggil; Choi, Youngjin

doi:10.1109/URAI.2017.7992763

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

Gang, Taeho;
Cho, Younggil;
Choi, Youngjin

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3

초록

The paper presents a method to classify electromyo-graphic (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%.

키워드

Electromyography (EMG); muscle activation; multi-layer perceptron (MLP); posture classification

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

저자: Gang, Taeho; Cho, Younggil; Choi, Youngjin

DOI: 10.1109/URAI.2017.7992763

발행일: 2017-07

유형: Proceedings Paper

저널명: 2017 14TH INTERNATIONAL CONFERENCE ON UBIQUITOUS ROBOTS AND AMBIENT INTELLIGENCE (URAI)

페이지: 406 ~ 407

언어: ENG

출판사: IEEE

발행국가: 미국

분량: 2 페이지

ISSN: P 2325-033X