Foot Postures Classification using sEMG Signals
- Authors
- 최영진
- Issue Date
- Jun-2019
- Publisher
- KROS
- Citation
- 16th International Conference on Ubiquitous Robots (UR2019), pp.541 - 543
- Journal Title
- 16th International Conference on Ubiquitous Robots (UR2019)
- Start Page
- 541
- End Page
- 543
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/2859
- 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%.
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- Appears in
Collections - COLLEGE OF ENGINEERING SCIENCES > SCHOOL OF ELECTRICAL ENGINEERING > 1. Journal Articles
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