A novel procedure to automate the removal of PLI and motion artifacts using mode decomposition to enhance pattern recognition of sEMG signals for myoelectric control of prosthesis.

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  • Additional Information
    • Source:
      Publisher: IOP Publishing Ltd Country of Publication: England NLM ID: 101675002 Publication Model: Electronic Cited Medium: Internet ISSN: 2057-1976 (Electronic) Linking ISSN: 20571976 NLM ISO Abbreviation: Biomed Phys Eng Express Subsets: MEDLINE
    • Publication Information:
      Original Publication: Bristol : IOP Publishing Ltd., [2015]-
    • Subject Terms:
      Electromyography*/methods ; Artifacts* ; Hand*/physiology ; Movement* ; Signal Processing, Computer-Assisted* ; Pattern Recognition, Automated*/methods ; Algorithms*; Humans ; Male ; Muscle Contraction ; Adult ; Artificial Limbs ; Female ; Motion ; Muscle, Skeletal/physiology
    • Abstract:
      Hand Movement Recognition (HMR) with sEMG is crucial for artificial hand prostheses. HMR performance mostly depends on the feature information that is fed to the classifiers. However, sEMG often captures noise like power line interference (PLI) and motion artifacts. This may extract redundant and insignificant feature information, which can degrade HMR performance and increase computational complexity. This study aims to address these issues by proposing a novel procedure for automatically removing PLI and motion artifacts from experimental sEMG signals. This will make it possible to extract better features from the signal and improve the categorization of various hand movements. Empirical mode decomposition and energy entropy thresholding are utilized to select relevant mode components for artifact removal. Time domain features are then used to train classifiers (kNN, LDA, SVM) for hand movement categorization, achieving average accuracies of 92.36%, 93.63%, and 98.12%, respectively, across subjects. Additionally, muscle contraction efforts are classified into low, medium, and high categories using this technique. Validation is performed on data from ten subjects performing eight hand movement classes and three muscle contraction efforts with three surface electrode channels. Results indicate that the proposed preprocessing improves average accuracy by 9.55% with the SVM classifier, significantly reducing computational time.
      (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)
    • Contributed Indexing:
      Keywords: Electromyography; PLI and motion artifacts; energy entropy; machine learning; mode decomposition; myoelectric; prosthesis
    • Publication Date:
      Date Created: 20240904 Date Completed: 20240912 Latest Revision: 20241030
    • Publication Date:
      20241031
    • Accession Number:
      10.1088/2057-1976/ad773a
    • Accession Number:
      39231462