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A Subject-Transfer Framework Based on Single-Trial EMG Analysis Using Convolutional Neural Networks

Authors
Kim, Keun-TaeGuan, CuntaiLee, Seong-Whan
Issue Date
1월-2020
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Subject-transfer framework; myoelectric interfaces; electromyography; convolutional neural networks
Citation
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, v.28, no.1, pp.94 - 103
Indexed
SCIE
SCOPUS
Journal Title
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
Volume
28
Number
1
Start Page
94
End Page
103
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/58550
DOI
10.1109/TNSRE.2019.2946625
ISSN
1534-4320
Abstract
In recent years, electromyography (EMG)-based practical myoelectric interfaces have been developed to improve the quality of daily life for people with physical disabilities. With these interfaces, it is very important to decode a user's movement intention, to properly control the external devices. However, improving the performance of these interfaces is difficult due to the high variations in the EMG signal patterns caused by intra-user variability. Therefore, this paper proposes a novel subject-transfer framework for decoding hand movements, which is robust in terms of intra-user variability. In the proposed framework, supportive convolutional neural network (CNN) classifiers, which are pre-trained using the EMG data of several subjects, are selected and fine-tuned for the target subject via single-trial analysis. Then, the target subject's hand movements are classified by voting the outputs of the supportive CNN classifiers. The feasibility of the proposed framework is validated with NinaPro databases 2 and 3, which comprise 49 hand movements of 40 healthy and 11 amputee subjects, respectively. The experimental results indicate that, when compared to the self-decoding framework, which uses only the target subject's data, the proposed framework can successfully decode hand movements with improved performance in both healthy and amputee subjects. From the experimental results, the proposed subject-transfer framework can be seen to represent a useful tool for EMG-based practical myoelectric interfaces controlling external devices.
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