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Evaluating EMG Feature and Classifier Selection for Application to Partial-Hand Prosthesis Control
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Evaluating EMG Feature and Classifier Selection for Application to Partial-Hand Prosthesis Control
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Evaluating EMG Feature and Classifier Selection for Application to Partial-Hand Prosthesis Control
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Journal Article
Evaluating EMG Feature and Classifier Selection for Application to Partial-Hand Prosthesis Control
2016
Overview
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 (
< 0.001) but no further significant decrease in error occurred when more than 4, 2, or 3 positions were included for the extrinsic (
= 0.07), intrinsic (
= 0.06), or combined extrinsic and intrinsic muscle EMG (
= 0.08), respectively. Finally, we found that a feature set determined by selecting optimal features from each channel outperformed the commonly used time domain (
< 0.001) and time domain/autoregressive feature sets (
< 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.
