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Statistical feature and channel selection for upper limb classification using sEMG signal processing

Statistical feature and channel selection for upper limb classification using sEMG signal processing PurposeFeature and channel selection stage plays an important role in identifying movement intent through surface electromyography (sEMG), once the best feature or features set and channel ensemble is still an open topic in this research field. In this work, we present three different strategies to feature and channel selection used in movement recognition through sEMG processing. This paper analyzes the feature selection stage, considering the relevance of each feature-channel pair to movement characterization.MethodsThree feature selection methods based on support vector machine recursive feature elimination (SVM-RFE), Monte Carlo feature selection, and singular value decomposition (SVD) entropy that are benchmark techniques for DNA microarray data analysis formed a ranking of feature/channel pairs. The ranking, organized according to their importance to movement classification, forms a heuristic for a wrapper method based feature selection algorithm, which uses the regularized extreme learning machine (RELM) classifier for the movement recognition. To test the proposed methods, we used the NINAPro Databases 2 and 3 (40 intact and 10 amputee volunteers, respectively).ResultsThe results derived from the technique reached average accuracies of 84.9%, 84.0%, and 83.9% for the SVM-RFE, Monte Carlo, and SVD entropy ranking methods, respectively, for database 2. At the same time, the same methods achieved 74.8%, 74.5%, and 74.1% of accuracy for database 3, respectively. In terms of most selected features, zero crossings (TZC), mean frequency (FMN), signal slope changes (TSLPCH2), and power (TPWR) were the most frequently selected.ConclusionThe results obtained showed no statistically significant difference among the three feature selection algorithms implemented regarding the classification accuracy. The introduction of the pre-processing steps in the classification process provided promising classification accuracy levels, outperforming the results presented by related works with the same NINAPro database. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research on Biomedical Engineering Springer Journals

Statistical feature and channel selection for upper limb classification using sEMG signal processing

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References (56)

Publisher
Springer Journals
Copyright
Copyright © Sociedade Brasileira de Engenharia Biomedica 2020
ISSN
2446-4732
eISSN
2446-4740
DOI
10.1007/s42600-020-00080-w
Publisher site
See Article on Publisher Site

Abstract

PurposeFeature and channel selection stage plays an important role in identifying movement intent through surface electromyography (sEMG), once the best feature or features set and channel ensemble is still an open topic in this research field. In this work, we present three different strategies to feature and channel selection used in movement recognition through sEMG processing. This paper analyzes the feature selection stage, considering the relevance of each feature-channel pair to movement characterization.MethodsThree feature selection methods based on support vector machine recursive feature elimination (SVM-RFE), Monte Carlo feature selection, and singular value decomposition (SVD) entropy that are benchmark techniques for DNA microarray data analysis formed a ranking of feature/channel pairs. The ranking, organized according to their importance to movement classification, forms a heuristic for a wrapper method based feature selection algorithm, which uses the regularized extreme learning machine (RELM) classifier for the movement recognition. To test the proposed methods, we used the NINAPro Databases 2 and 3 (40 intact and 10 amputee volunteers, respectively).ResultsThe results derived from the technique reached average accuracies of 84.9%, 84.0%, and 83.9% for the SVM-RFE, Monte Carlo, and SVD entropy ranking methods, respectively, for database 2. At the same time, the same methods achieved 74.8%, 74.5%, and 74.1% of accuracy for database 3, respectively. In terms of most selected features, zero crossings (TZC), mean frequency (FMN), signal slope changes (TSLPCH2), and power (TPWR) were the most frequently selected.ConclusionThe results obtained showed no statistically significant difference among the three feature selection algorithms implemented regarding the classification accuracy. The introduction of the pre-processing steps in the classification process provided promising classification accuracy levels, outperforming the results presented by related works with the same NINAPro database.

Journal

Research on Biomedical Engineering – Springer Journals

Published: Aug 18, 2020

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