Paper Info
Title
Entropy Of Surface Emg Reflects Object Weight In Grasp-And-Lift Task
Abstract
Fingertip force coordination is crucial to the success of grasp-and-lift tasks. In the development of motor prosthesis for daily applications, the ability to accurately classify the desired grasp-and-lift from multi-channel surface electromyography (sEMG) is essential. In order to extract reliable indicators for fingertip force coordination, we searched an extensive set of sEMG features for the optimal subset of relevant features. Using mutual information based feature selection we found that a subset of not more than 10 sEMG features selected from over seven thousand, could effectively classify object weights in grasp-and-lift tasks. Average classification accuracies of 82.53% in the acceleration phase and 88.61% in the isometric contraction phase were achieved. Furthermore, sEMG features associated with object weights and common across individuals were identified. These time-domain features (entropy, mean/median absolute deviation, pNNx) can be calculated efficiently, providing possible new indicators.
Year
DOI
Venue
2017
10.1109/EMBC.2017.8037372
2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Computer vision,Lift (force),GRASP,Pattern recognition,Feature selection,Computer science,Electromyography,Median absolute deviation,Feature extraction,Mutual information,Acceleration,Artificial intelligence
Conference
2017
ISSN
Citations 
PageRank 
1094-687X
0
0.34
References 
Authors
6
3
Name
Order
Citations
PageRank
Yuqi Li1267.16
Beth Jelfs2629.40
Rosa H M Chan318222.79