Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 최영진 | - |
dc.date.accessioned | 2020-02-14T06:24:56Z | - |
dc.date.available | 2020-02-14T06:24:56Z | - |
dc.date.issued | 2019-06 | - |
dc.identifier.citation | 2019 16th International Conference on Ubiquitous Robots (UR), Page. 541-543 | en_US |
dc.identifier.uri | https://scholar.google.co.kr/scholar?hl=ko&as_sdt=0%2C5&q=%22Foot+Postures+Classification+using+sEMG+Signals%22&btnG= | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/125326 | - |
dc.description.abstract | The paper proposes an approach to classify three target foot postures from sEMG (surface electromyography) signals measured around right lower leg. A band-type fabric sensor is utilized to acquire sEMG signals for training and realtime testing, respectively. To implement a classifier of target foot postures, a machine learning algorithm using multi-layer perceptron (known as an artificial neural network) is utilized for the sEMG signals. Experimental result shows that the proposed scheme is effective with an overall accuracy 96%. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | KROS | en_US |
dc.title | Foot Postures Classification using sEMG Signals | en_US |
dc.type | Article | en_US |
dc.relation.page | 541-543 | - |
dc.contributor.googleauthor | Choi, Yuna | - |
dc.contributor.googleauthor | Yang, Sedong | - |
dc.contributor.googleauthor | Lee, Seulah | - |
dc.contributor.googleauthor | Choi, Youngjin | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF ENGINEERING SCIENCES[E] | - |
dc.sector.department | DIVISION OF ELECTRICAL ENGINEERING | - |
dc.identifier.pid | cyj | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.