Paper Info
Title
Increasing The Robustness Against Force Variation In Emg Motion Classification By Common Spatial Patterns
Abstract
In the practical use of an electromyography (EMG) pattern-recognition based myoelectric prosthesis, the variation of force levels to do a motion would be inevitable, which will cause a change of EMG patterns. Therefore, the force variation will decay the performance of a trained classifier. In this study, the common spatial pattern (CSP) method was proposed with an attempt to improve the robustness of EMG-PR based classifier against force variation. The EMG signals were acquired from three able-bodied subjects when they were performing the motions at low, medium, and high force levels, respectively. And in the pattern recognition, CSP features were extracted from the EMG signals for motion classification. By comparing the classification accuracies between the CSP and the commonly used time-domain (TD) features, the CSP features showed a better robustness against force variation with an increment of 5.3% of the average classification accuracy. Especially, the classification accuracy of a classifier was 84.2% when tested at low force level by using CSP features, which was 18.5% higher than that of the TD features. These preliminary results suggest that using CSP features may increase the robustness of EMG-based myoelectric control.
Year
DOI
Venue
2017
10.1109/EMBC.2017.8036848
2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Pattern recognition,Computer science,Electromyography,Robustness (computer science),Speech recognition,Artificial intelligence,Classifier (linguistics),Spatial ecology
Conference
2017
ISSN
Citations 
PageRank 
1094-687X
2
0.39
References 
Authors
4
4
Name
Order
Citations
PageRank
Xiangxin Li1458.34
Peng Fang23015.63
Lan Tian364.38
Guanglin Li431457.23