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Classification of Rock-Paper-Scissors using Electromyography and Multi-Layer Perceptron
- Authors
- Gang, Taeho; Cho, Younggil; Choi, Youngjin
- Issue Date
- Jul-2017
- Publisher
- IEEE
- Keywords
- Electromyography (EMG); muscle activation; multi-layer perceptron (MLP); posture classification
- Citation
- 2017 14TH INTERNATIONAL CONFERENCE ON UBIQUITOUS ROBOTS AND AMBIENT INTELLIGENCE (URAI), pp 406 - 407
- Pages
- 2
- Indexed
- SCIE
SCOPUS
- Journal Title
- 2017 14TH INTERNATIONAL CONFERENCE ON UBIQUITOUS ROBOTS AND AMBIENT INTELLIGENCE (URAI)
- Start Page
- 406
- End Page
- 407
- ISSN
- 2325-033X
- Abstract
- 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%.
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Related Researcher
- Choi, Youngjin
- ERICA 공학대학 (DEPARTMENT OF ROBOT ENGINEERING)