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Human lower limb activity recognition techniques, databases, challenges and its applications using sEMG signal: an

Ankit Vijayvargiya1,2, Bharat Singh1, Rajesh Kumar1

  • 1Department of Electrical Engineering, Malaviya National Institute of Technology, Jaipur, India.

Biomedical Engineering Letters
|October 14, 2022
PubMed
Summary

This study reviews techniques for processing surface electromyography (sEMG) signals for human lower limb activity recognition. It highlights methods for artifact removal, data processing, and classification to improve accuracy in applications like prosthetics and diagnostics.

Keywords:
Biomedical signal processingHuman lower limb activity recognitionHuman-machine interactionMachine learning techniquesSurface electromyography signal

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Signal Processing

Background:

  • Human lower limb activity recognition (HLLAR) is crucial for applications in neuromuscular disorders, security, robotics, and prosthetics.
  • Surface electromyography (sEMG) offers advantages for HLLAR due to its responsiveness and non-invasive nature.
  • sEMG signals are inherently noisy, necessitating robust pre-processing for accurate analysis.

Purpose of the Study:

  • To provide a comprehensive overview of techniques for human lower limb activity recognition using sEMG signals.
  • To address the challenges associated with noise in sEMG data for lower limb applications.
  • To survey existing datasets and processing/classification methods for sEMG-based HLLAR.

Main Methods:

  • Review of artifact elimination techniques for lower limb sEMG.
  • Survey of available lower limb sEMG datasets.
  • Description of sEMG data processing and classification methodologies.

Main Results:

  • Identified key techniques for sEMG artifact removal.
  • Cataloged existing datasets for lower limb sEMG research.
  • Summarized various processing and classification approaches for HLLAR.

Conclusions:

  • Pre-processing sEMG signals is essential for consistent and precise evaluation in HLLAR applications.
  • The presented framework can advance sEMG-based human lower limb activity recognition.
  • Future research directions in sEMG-based HLLAR are identified.