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Training-Free Bayesian Self-Adaptive Classification for sEMG Pattern Recognition Including Motion Transition SCIE SSCI SCOPUS

Title
Training-Free Bayesian Self-Adaptive Classification for sEMG Pattern Recognition Including Motion Transition
Authors
PARK, SEONG SIKCHUNG, WAN KYUNKIM, KEE HOON
Date Issued
2020-06
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Abstract
A direct, ready-to-use surface electromyogram (sEMG) pattern classification algorithm that does not require prerequisite training, regardless of the user, is proposed herein. In addition to data collection, conventional supervised learning approaches for sEMG require labeling and segmenting the data and additional time for the learning algorithm. Consequently, these approaches cannot cope well with sEMG patterns during motion transitions of various movement speeds. The proposed unsupervised and self-adaptive method employs an iterative self-adaptive procedure realized by the probabilistic methods of diffusion, updating, and registration to cluster the activation patterns simultaneously in real time, and classify the current sEMG as new clustered patterns. Experiments demonstrated that even for the same motion, the proposed method could autonomously detect changes in muscular activation patterns varying with the speed of motion. Furthermore, some patterns of both steady- and transient-state motions could be distinguished. In addition, it was verified that the classified sEMG pattern could be correlated consistently with the actual motion, thereby realizing a high level of motion classification.
URI
https://oasis.postech.ac.kr/handle/2014.oak/100300
DOI
10.1109/TBME.2019.2947089
ISSN
0018-9294
Article Type
Article
Citation
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, vol. 67, no. 6, page. 1775 - 1786, 2020-06
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정완균CHUNG, WAN KYUN
Dept of Mechanical Enginrg
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