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Related Experiment Video

Updated: Jan 23, 2026

Extraction of the EPP Component from the Surface EMG
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An Android Application for Estimating Muscle Onset Latency using Surface EMG Signal.

M Karimpour1,2, H Parsaei3,4, Z Rojhani-Shirazi5

  • 1School of Management & Medical Information Sciences, Health Human Resources Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.

Journal of Biomedical Physics & Engineering
|June 20, 2019
PubMed
Summary

This study developed a portable Android app for estimating Muscle Onset Latency (MOL) from Electromyography (EMG) signals. The app offers a cost-effective and accessible solution for rehabilitation sciences and nerve conduction studies.

Keywords:
Android applicationMuscle Onset LatencyMuscle Onset Latency EstimationSurface EMG signal analysisElectromyography

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Science
  • Signal Processing

Background:

  • Electromyography (EMG) signal processing and Muscle Onset Latency (MOL) are crucial in rehabilitation and nerve conduction studies.
  • Existing software for MOL estimation is typically desktop-based, limiting portability and increasing costs.
  • There is a need for accessible and portable tools for analyzing EMG signals.

Purpose of the Study:

  • To develop a non-expensive and portable Android application for estimating MOL from surface EMG signals.
  • To provide a mobile solution for EMG signal analysis in clinical and academic settings.
  • To enhance the accessibility of EMG analysis tools.

Main Methods:

  • A multi-layer architecture was designed for the Android application.
  • Android-based algorithms were implemented for EMG signal analysis and MOL estimation.
  • A user-friendly graphical user interface (GUI) was developed for simplified analysis.

Main Results:

  • The developed Android app demonstrated promising performance in estimating MOL.
  • MOL values estimated by the app were statistically comparable to a commercial Windows-based software (MegaWin 3.0).
  • High linear correlation (coefficient ~ 0.93) and low relative error (<10%) were observed between the app and commercial software.

Conclusions:

  • Portable smart devices like Android phones can significantly reduce the cost and increase the convenience of biomedical signal analysis.
  • The developed Android app is a viable and promising tool for estimating MOL from surface EMG signals.
  • The app is suitable for educational and research purposes in rehabilitation sciences.