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Optimized strategies for developing high-speed muscle activity monitors utilizing multi-resolution energy operator.

Shenglin Wang1, Guosheng Zhao1, Yiwei Liao1

  • 1College of Computer Science and Information Engineering, Harbin Normal University, Harbin, China.

Frontiers in Bioengineering and Biotechnology
|April 16, 2025
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Summary

A new method using the multi-resolution energy operator (MTEO) improves electromyographic (EMG) signal activity detection. This approach enhances accuracy and efficiency for EMG analysis in various fields.

Keywords:
EMG activity monitorTeager-Kaiser energy operator (TKEO)change-point detectionconvolutional neural network (CNN)double-threshold detector

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Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Human-Machine Interaction

Background:

  • Electromyographic (EMG) signal analysis is crucial for medical diagnostics, sports science, and human-machine interaction.
  • Current research prioritizes EMG signal recognition over precise onset/offset detection.
  • Accurate detection of muscle activity change-points in EMG signals requires further investigation.

Purpose of the Study:

  • To introduce a novel method for detecting EMG signal activity using a variant of the Teager-Kaiser energy operator (TKEO).
  • To develop and evaluate two EMG activity detection strategies based on the multi-resolution energy operator (MTEO).

Main Methods:

  • Proposed a multi-resolution energy operator (MTEO) as a novel approach for EMG signal activity detection.
  • Developed a threshold-based detector (MEOTD) and a convolutional neural network-mimicking detector (MEONND) using MTEO.
  • Employed the Analytic Hierarchy Process (AHP) for semi-subjective evaluation of detector performance on real EMG data.

Main Results:

  • The MTEO demonstrated superior preprocessing capabilities for EMG signals.
  • MTEO-based detectors (MEOTD and MEONND) exhibited enhanced reliability and accuracy.
  • The MEONND achieved a balance of computational efficiency and high accuracy.

Conclusions:

  • The proposed MTEO-based method improves EMG signal activity detection quality and efficiency without increased algorithmic complexity.
  • This technique offers a valuable tool for diverse applications including ergonomics, human-machine interaction, and biomedical engineering.
  • The study highlights the potential of MTEO for advancing EMG signal analysis.