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Kernel Density Estimation of Electromyographic Signals and Ensemble Learning for Highly Accurate Classification of a Large Set of Hand/Wrist Motions

Authors
  • Ghaderi, Parviz1, 2
  • Nosouhi, Marjan1
  • Jordanic, Mislav3
  • Marateb, Hamid Reza1, 3
  • Mañanas, Miguel Angel3, 4
  • Farina, Dario5
  • 1 The Biomedical Engineering Department, Engineering Faculty, University of Isfahan, Isfahan
  • 2 Laboratory of Sensory Processing, Brain Mind Institute, Faculty of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne , (Switzerland)
  • 3 Biomedical Engineering Research Centre (CREB), Automatic Control Department (ESAII), Universitat Politècnica de Catalunya-Barcelona Tech (UPC), Barcelona , (Spain)
  • 4 CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid , (Spain)
  • 5 Department of Bioengineering, Imperial College London, London , (United Kingdom)
Type
Published Article
Journal
Frontiers in Neuroscience
Publisher
Frontiers Media SA
Publication Date
Mar 09, 2022
Volume
16
Identifiers
DOI: 10.3389/fnins.2022.796711
Source
Frontiers
Keywords
Disciplines
  • Neuroscience
  • Original Research
License
Green

Abstract

The performance of myoelectric control highly depends on the features extracted from surface electromyographic (sEMG) signals. We propose three new sEMG features based on the kernel density estimation. The trimmed mean of density (TMD), the entropy of density, and the trimmed mean absolute value of derivative density were computed for each sEMG channel. These features were tested for the classification of single tasks as well as of two tasks concurrently performed. For single tasks, correlation-based feature selection was used, and the features were then classified using linear discriminant analysis (LDA), non-linear support vector machines, and multi-layer perceptron. The eXtreme gradient boosting (XGBoost) classifier was used for the classification of two movements simultaneously performed. The second and third versions of the Ninapro dataset (conventional control) and Ameri’s movement dataset (simultaneous control) were used to test the proposed features. For the Ninapro dataset, the overall accuracy of LDA using the TMD feature was 98.99 ± 1.36% and 92.25 ± 9.48% for able-bodied and amputee subjects, respectively. Using ensemble learning of the three classifiers, the average macro and micro-F-score, macro recall, and precision on the validation sets were 98.23 ± 2.02, 98.32 ± 1.93, 98.32 ± 1.93, and 98.88 ± 1.31%, respectively, for the intact subjects. The movement misclassification percentage was 1.75 ± 1.73 and 3.44 ± 2.23 for the intact subjects and amputees. The proposed features were significantly correlated with the movement classes [Generalized Linear Model (GLM); P-value < 0.05]. An accurate online implementation of the proposed algorithm was also presented. For the simultaneous control, the overall accuracy was 99.71 ± 0.08 and 97.85 ± 0.10 for the XGBoost and LDA classifiers, respectively. The proposed features are thus promising for conventional and simultaneous myoelectric control.

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