Updated ICA Weight Matrix for Lower Limb Myoelectric Classification

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

In the recent past, several pattern recognition and computational intelligence methods have been applied for both upper and lower limb data. However, still there exist issues due to the complex nature of muscles in the arm and body. This research proposes a classification scheme using updated independent component analysis (ICA) to consider the individual characteristics from multichannel surface electromyography (sEMG) on lower limb (for healthy subjects and subjects with knee pathology) data. Firstly, multichannel sEMG data was decomposed into various independent components (ICs) using an updated ICA weight matrix. Secondly, time domain features were extracted using ICA separated sources (ICs). The feature reduction and selection were carried out using Fischer discriminant analysis (FDA) and later classified using linear discriminant analysis (LDA). The average classification accuracy is greater than 85% and 75% for healthy and knee pathology subjects respectively.

Original languageEnglish
Title of host publicationBiomedical Signal Processing
Subtitle of host publicationA Modern Approach
EditorsGanesh R. Naik, Wellington Pinheiro dos Santos
Place of PublicationBoca Raton, FL
PublisherCRC Press/Balkema
Chapter10
Pages225-234
Number of pages10
ISBN (Electronic)9781000906462
ISBN (Print)9781032061917, 9781032061924
DOIs
Publication statusPublished - 2024

Publication series

NameBiomedical Signal and Image Processing
PublisherCRC Press

Keywords

  • Electromyography (EMG)
  • pattern recognition
  • ICA weight matrix
  • lower limb
  • independent component analysis (ICA)
  • sEMG (Surface Electromyography)
  • linear discriminant analysis (LDA)

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