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RETRACTED: Optimizing the impact of time domain segmentation techniques on upper limb EMG decoding using multimodal

Muhammad Faisal1, Ikramullah Khosa1, Asim Waris2

  • 1Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore Campus, Lahore, Pakistan.

Plos One
|May 8, 2025
PubMed
Summary
This summary is machine-generated.

The rectangular window technique significantly improved electromyography (EMG) decoding for upper limb movement classification, achieving 99.98% accuracy. This enhances myoelectric control systems for prosthetics and rehabilitation.

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Rehabilitation Technology

Background:

  • Neurological disorders cause widespread motor impairments.
  • Existing electromyography (EMG) decoding research focuses on classifiers and features, neglecting preprocessing techniques.
  • Time-domain windowing in EMG signal preprocessing is an understudied area.

Purpose of the Study:

  • To investigate the impact of different time-domain windowing techniques on electromyography (EMG) decoding accuracy for upper limb movements.
  • To identify the most effective windowing technique for classifying finger movements.
  • To bridge the knowledge gap regarding EMG preprocessing in motor function studies.

Main Methods:

  • Recorded surface EMG data from volunteers performing fifteen distinct finger movements.
  • Compared the performance of eight different time-domain windowing techniques.
  • Utilized 40 time-domain features and a Linear Support Vector Machine (L-SVM) classifier for movement classification.

Main Results:

  • The rectangular window technique demonstrated superior performance among the tested methods.
  • Achieved a classification accuracy of 99.98% using the rectangular window, 40 time-domain features, and an L-SVM classifier.
  • High accuracy confirms the efficacy of surface EMG for precise upper limb movement classification.

Conclusions:

  • The rectangular windowing technique is highly effective for EMG-based upper limb movement classification.
  • Optimized EMG preprocessing can significantly enhance the accuracy and reliability of myoelectric control systems.
  • Findings support advancements in prosthetic limbs, wearable sensors, human-computer interaction, and brain-computer interfaces.