Improving Myoelectric Pattern Recognition Robustness to Electrode Shift by Autoencoder

It is evident that the electrode shift will result in a degradation of myoelectric pattern recognition classification accuracy, which is inevitable during the prosthetic socket donning and doffing. To cope with this limitation, we propose an unsupervised feature extraction method called sparse autoe...

Full description

Saved in:
Bibliographic Details
Published in:Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Vol. 2018; pp. 5652 - 5655
Main Authors: Lv, Bo, Sheng, Xinjun, Zhu, Xiangyang
Format: Conference Proceeding Journal Article
Language:English
Published: United States IEEE 01.07.2018
Subjects:
Electrodes
Electromyography
Error analysis
Feature extraction
Muscles
Pattern recognition
Wrist
ISSN:1557-170X, 2694-0604, 1558-4615, 2694-0604
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:It is evident that the electrode shift will result in a degradation of myoelectric pattern recognition classification accuracy, which is inevitable during the prosthetic socket donning and doffing. To cope with this limitation, we propose an unsupervised feature extraction method called sparse autoencoder (SAE) to extract the robust spatial structure and correlation of high density (HD) electromyography (EMG). The algorithm is evaluated on nine intact-limbed subjects and one amputee. The experimental results show that SAE achieves lower classification error without shift, and significantly decrease the sensitivity to electrode shift with ±1 cm compared with the time-domain and autoregressive features (TDAR). Furthermore, SAE is not sensitive to the shift direction that is perpendicular to the muscle fibers. The promising results of this study make great contribution to promoting the applications of pattern recognition based myoelectric control system in real-world condition.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1557-170X
2694-0604
1558-4615
2694-0604
DOI:10.1109/EMBC.2018.8513525