TY - GEN
T1 - Movement intention decoding based on deep learning for multiuser myoelectric interfaces
AU - Park, Ki Hee
AU - Lee, Seong Whan
N1 - Publisher Copyright:
© 2016 IEEE.
Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.
PY - 2016/4/20
Y1 - 2016/4/20
N2 - Recently, the development of practical myoelectric interfaces has resulted in the emergence of wearable rehabilitation robots such as arm prosthetics. In this paper, we propose a novel method of movement intention decoding based on the deep feature learning using electromyogram of human biosignals. In daily life, the inter-user variability cause decreases in performance by modulating target EMG patterns across different users. Therefore, we propose a user-adaptive decoding method for robust movement intention decoding in the inter-user variability, employing the convolutional neural network for the deep feature learning, trained by different users. In our experimental results, the proposed method predicted hand movement intention more accurately than a competing method.
AB - Recently, the development of practical myoelectric interfaces has resulted in the emergence of wearable rehabilitation robots such as arm prosthetics. In this paper, we propose a novel method of movement intention decoding based on the deep feature learning using electromyogram of human biosignals. In daily life, the inter-user variability cause decreases in performance by modulating target EMG patterns across different users. Therefore, we propose a user-adaptive decoding method for robust movement intention decoding in the inter-user variability, employing the convolutional neural network for the deep feature learning, trained by different users. In our experimental results, the proposed method predicted hand movement intention more accurately than a competing method.
KW - Convolutional neural network
KW - Deep feature learning
KW - Electromyogram
KW - Myoelectric interfaces
UR - http://www.scopus.com/inward/record.url?scp=84969142146&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84969142146&partnerID=8YFLogxK
U2 - 10.1109/IWW-BCI.2016.7457459
DO - 10.1109/IWW-BCI.2016.7457459
M3 - Conference contribution
AN - SCOPUS:84969142146
T3 - 4th International Winter Conference on Brain-Computer Interface, BCI 2016
BT - 4th International Winter Conference on Brain-Computer Interface, BCI 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Winter Conference on Brain-Computer Interface, BCI 2016
Y2 - 22 February 2016 through 24 February 2016
ER -