Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | 4th International Winter Conference on Brain-Computer Interface, BCI 2016 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Print) | 9781467378413 |
| DOIs | |
| Publication status | Published - 2016 Apr 20 |
| Event | 4th International Winter Conference on Brain-Computer Interface, BCI 2016 - Gangwon Province, Korea, Republic of Duration: 2016 Feb 22 → 2016 Feb 24 |
Other
| Other | 4th International Winter Conference on Brain-Computer Interface, BCI 2016 |
|---|---|
| Country | Korea, Republic of |
| City | Gangwon Province |
| Period | 16/2/22 → 16/2/24 |
Fingerprint
Keywords
- Convolutional neural network
- Deep feature learning
- Electromyogram
- Myoelectric interfaces
ASJC Scopus subject areas
- Human-Computer Interaction
Cite this
Movement intention decoding based on deep learning for multiuser myoelectric interfaces. / Park, Ki Hee; Lee, Seong Whan.
4th International Winter Conference on Brain-Computer Interface, BCI 2016. Institute of Electrical and Electronics Engineers Inc., 2016. 7457459.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Movement intention decoding based on deep learning for multiuser myoelectric interfaces
AU - Park, Ki Hee
AU - Lee, Seong Whan
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
SN - 9781467378413
BT - 4th International Winter Conference on Brain-Computer Interface, BCI 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -