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Compressed sensing study for the sEMG data of SCI survivors.

Zongxian Feng1, Beining Cui2, Fan He1

  • 1Department of Orthopedics, Ningbo Medical Center Lihuili Hospital, Ningbo, Zhejiang Province, China.

Computer Methods in Biomechanics and Biomedical Engineering
|September 8, 2025
PubMed
Summary

Surface electromyography (sEMG) can evaluate walking function. Compressed sensing (CS) with regularized orthogonal matching pursuit (ROMP) and a specific matrix improved sEMG signal reconstruction for spinal cord injury (SCI) patients.

Keywords:
Spinal cord injury (SCI)compressed sensing (CS)orthogonal matching pursuit (OMP)regularized orthogonal matching pursuit (ROMP)surface electromyography (sEMG)

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Signal Processing

Background:

  • Surface electromyography (sEMG) is crucial for assessing walking function.
  • Compressed sensing (CS) reduces data acquisition needs by exploiting signal sparsity.
  • Spinal cord injury (SCI) impacts motor control, necessitating effective evaluation methods.

Purpose of the Study:

  • To introduce and evaluate a novel compressed sensing (CS) algorithm for surface electromyography (sEMG) signal reconstruction in spinal cord injury (SCI) patients.
  • To compare the performance of the proposed regularized orthogonal matching pursuit (ROMP) algorithm against the conventional orthogonal matching pursuit (OMP) algorithm.
  • To investigate the influence of different measurement matrices on the accuracy of sEMG signal reconstruction.

Main Methods:

  • Development of a compressed sensing (CS) algorithm for sEMG signals using regularized orthogonal matching pursuit (ROMP).
  • Reconstruction of multiple sEMG signals from spinal cord injury (SCI) subjects using the ROMP algorithm.
  • Comparative analysis of ROMP with orthogonal matching pursuit (OMP) and evaluation of various measurement matrices, including the binary permuted block diagonal (BPBD) matrix.

Main Results:

  • The regularized orthogonal matching pursuit (ROMP) algorithm demonstrated effective reconstruction of sEMG signals from SCI patients.
  • The combination of ROMP with a binary permuted block diagonal (BPBD) matrix yielded superior reconstruction accuracy compared to the standard OMP algorithm.
  • Different measurement matrices significantly impacted the reconstruction performance of the CS algorithms.

Conclusions:

  • The proposed ROMP-based CS algorithm offers a promising approach for efficient sEMG data acquisition in walking function evaluation for SCI individuals.
  • The BPBD matrix enhances the performance of CS algorithms, suggesting its utility in sEMG-based rehabilitation monitoring.
  • This study highlights the potential of advanced signal processing techniques to improve the assessment and management of SCI.