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FPGA-Parallelized Digital Filtering for Real-Time Linear Envelope Detection of Surface Electromyography Signal on

Abdelouahad Achmamad1, Atman Jbari2, Nourdin Yaakoubi1

  • 1Laboratoire d'Acoustique de l'Universite du Mans (LAUM), UMR CNRS 6613, Institut d'Acoustique-Graduate School (IA-GS), CNRS, Le Mans Universite, 72085 Le Mans, France.

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Summary
This summary is machine-generated.

This study introduces an optimized framework for real-time linear envelope detection of surface electromyography (sEMG) signals. The new Butterworth low-pass filter design offers improved accuracy and reduced hardware usage compared to traditional methods.

Keywords:
CompactRIOFPGALabVIEWdiscrete implementation structureelectromyographylinear envelopelow pass filtermoving average filter

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Embedded Systems

Background:

  • Surface electromyography (sEMG) is crucial for medical diagnosis and human-machine interfaces.
  • Accurate linear envelope detection of sEMG signals requires efficient processing pipelines.
  • Existing methods may not meet real-time performance demands for complex applications.

Purpose of the Study:

  • To implement an optimized processing framework for real-time linear envelope detection of sEMG signals.
  • To design and evaluate a novel Butterworth low-pass filter for sEMG processing on an FPGA.
  • To compare the proposed filter's performance against the conventional Moving Average (MAV) filter.

Main Methods:

  • Developed a real-time sEMG processing pipeline including data acquisition, full-wave rectification, and low-pass filtering.
  • Implemented a parallel second-order Butterworth low-pass (LP) filter on an FPGA core.
  • Evaluated filter performance using Mean Square Error (MSE) and hardware resource consumption.
  • Compared the proposed LP filter against the Moving Average (MAV) filter.

Main Results:

  • The proposed Butterworth LP filter achieved a deterministic execution time of 98 ns per sample, significantly faster than the acquisition interval.
  • The designed LP filter demonstrated improved Mean Square Error (MSE) compared to the MAV filter.
  • The proposed filter design resulted in reduced hardware resource consumption on the FPGA.
  • The real-time performance was guaranteed with a processing speed two orders of magnitude faster than the data acquisition sample interval.

Conclusions:

  • The proposed optimized processing framework enables efficient and accurate real-time linear envelope detection of sEMG signals.
  • The novel Butterworth LP filter design is a valid and reliable alternative to conventional methods like MAV.
  • The implementation on FPGA offers high accuracy with minimal hardware resource utilization, suitable for embedded systems.