Paper Info
Title
Evaluating EMG Feature and Classifier Selection for Application to Partial-Hand Prosthesis Control.
Abstract
Pattern recognition-based myoelectric control of upper-limb prostheses has the potential to restore control of multiple degrees of freedom. Though this control method has been extensively studied in individuals with higher-level amputations, few studies have investigated its effectiveness for individuals with partial-hand amputations. Most partial-hand amputees retain a functional wrist and the ability of pattern recognition-based methods to correctly classify hand motions from different wrist positions is not well studied. In this study, focusing on partial-hand amputees, we evaluate (1) the performance of non-linear and linear pattern recognition algorithms and (2) the performance of optimal EMG feature subsets for classification of four hand motion classes in different wrist positions for 16 non-amputees and 4 amputees. Our results show that linear discriminant analysis and linear and non-linear artificial neural networks perform significantly better than the quadratic discriminant analysis for both non-amputees and partial-hand amputees. For amputees, including information from multiple wrist positions significantly decreased error (p < 0.001) but no further significant decrease in error occurred when more than 4, 2, or 3 positions were included for the extrinsic (p = 0.07), intrinsic (p = 0.06), or combined extrinsic and intrinsic muscle EMG (p = 0.08), respectively. Finally, we found that a feature set determined by selecting optimal features from each channel outperformed the commonly used time domain (p < 0.001) and time domain/ autoregressive feature sets (p < 0.01). This method can be used as a screening filter to select the features from each channel that provide the best classification of hand postures across different wrist positions.
Year
DOI
Venue
2016
10.3389/fnbot.2016.00015
FRONTIERS IN NEUROROBOTICS
Keywords
Field
DocType
pattern recognition,electromyography,partial-hand amputee,myoelectric control,intrinsic hand muscles,feature selection
Time domain,Autoregressive model,Wrist,Feature selection,Computer science,Speech recognition,Linear discriminant analysis,Classifier (linguistics),Artificial neural network,Quadratic classifier
Journal
Volume
ISSN
Citations 
10
1662-5218
7
PageRank 
References 
Authors
0.52
10
3
Name
Order
Citations
PageRank
Adenike A. Adewuyi170.52
Levi J Hargrove243842.47
Todd A. Kuiken31329.41