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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