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PaWFE: Fast Signal Feature Extraction Using Parallel Time Windows.

Manfredo Atzori1, Henning Müller1,2

  • 1Information Systems Institute, University of Applied Sciences Western Switzerland (HES-SO Valais), Sierre, Switzerland.

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|September 26, 2019
PubMed
Summary
This summary is machine-generated.

Researchers developed a fast signal feature extraction algorithm to accelerate the development of machine learning for robotic prosthetics. This parallel window feature extractor (PaWFE) significantly reduces computational time, enabling quicker analysis of biosignals.

Keywords:
classificationfeature extractionhand prostheticsmachine learningsignal processingsurface electromyography

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Area of Science:

  • Biomedical Engineering
  • Machine Learning
  • Signal Processing

Background:

  • Hand amputations significantly impact quality of life.
  • Surface electromyography (sEMG) and machine learning are key for controlling advanced prosthetic hands.
  • Current computational demands hinder rapid development and real-time application.

Purpose of the Study:

  • To develop a rapid signal feature extraction algorithm for sEMG data.
  • To enable easy integration of new feature extraction methods.
  • To accelerate machine learning model development for prosthetic control.

Main Methods:

  • Introduced PaWFE (Parallel Window Feature Extractor) for parallel processing of time windows.
  • Implemented in MATLAB, supporting various time and frequency domain features.
  • Benchmarked on the Ninapro database using multiple threads and significant RAM.

Main Results:

  • Achieved up to 20x computational time reduction with 32 cores, demonstrating excellent scalability.
  • Feature extraction completed in seconds for entire datasets and under 100 ms for single windows.
  • Reduced feature extraction time by over 15x compared to traditional methods.

Conclusions:

  • PaWFE significantly accelerates biosignal feature extraction for machine learning applications.
  • The tool facilitates rapid testing of machine learning models on large datasets.
  • PaWFE is a valuable resource for both research and potential real-time prosthetic control.