Paper Info
Title
Robust feature sets for contraction level invariant control of upper limb myoelectric prosthesis.
Abstract
•Feature sets robust against force level variations in pattern recognition based control of myoelectric upper limb prosthesis have been proposed.•Significance of using non-linear techniques to analyze complex signals like EMG, which are both non-linear and non-stationary has been highlighted here.•With a significant improvement in classification performance of about 7% to 9%, the proposed feature sets, FFS and PSEAR outperformed all the other well established feature sets for contraction level invariant myoelectric control, considered in this paper.•Improvement in performance was observed not only for flexion movements but also for prehension and power grip movements.•The proposed feature sets can be potential candidates to make the clinical implementation of intuitive PR-based myoelectric prosthetic control possible in the future.
Year
DOI
Venue
2019
10.1016/j.bspc.2019.02.010
Biomedical Signal Processing and Control
Keywords
Field
DocType
Electromyogram,Myoelectric prosthesis,Pattern recognition,Contraction level variation,Fractal analysis,Entropy
Prosthesis,Computer vision,Upper limb,Pattern recognition,Feature extraction,Invariant (mathematics),Artificial intelligence,Rehabilitation engineering,Mathematics
Journal
Volume
ISSN
Citations 
51
1746-8094
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Nisheena V. Iqbal100.34
Kamalraj Subramaniam245.15
Shaniba Asmi P.300.34