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Related Experiment Video

Updated: Sep 6, 2025

Using Facial Electromyography to Assess Facial Muscle Reactions to Experienced and Observed Affective Touch in Humans
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Relationship between EMG features and force in orbicularis oris muscle.

Lan Wang1, Yanqi Wu2, Min Zhu2

  • 1Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
|June 27, 2022
PubMed
Summary

This study quantifies electromyography (EMG) and force in the orbicularis oris muscle. Findings suggest a third-order model with FuzzyEn can guide muscle functional training for improved outcomes.

Keywords:
FuzzyEnOrbicularis oris musclemuscle forcesurface electromyography

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Area of Science:

  • Biomedical Engineering
  • Clinical Physiology
  • Rehabilitation Science

Background:

  • Lip incompetence due to mouth breathing is common.
  • Current muscle functional training lacks objective intensity and amplitude indicators.
  • This deficiency leads to suboptimal training results.

Purpose of the Study:

  • To quantify the electromyography (EMG) and force relationship in the orbicularis oris muscle.
  • To enable evaluation of muscle functional training indicators using EMG signals.
  • To enhance the effectiveness of orofacial muscle training.

Main Methods:

  • Simultaneous recording of EMG and force signals from the orbicularis oris muscle (0-100% MVC) in 12 healthy subjects.
  • Analysis of four EMG features: RMS, WAMP, SampEn, and FuzzyEn.
  • Regression analysis using first-order and third-order polynomial models.

Main Results:

  • High correlations were observed between EMG features and muscle force across both models.
  • The third-order polynomial model demonstrated a significantly higher coefficient of determination (R2) than the linear model (p<0.001).
  • FuzzyEn exhibited the highest R2 value (0.884 ± 0.059) among the analyzed EMG features.

Conclusions:

  • A third-order regression model incorporating FuzzyEn from EMG signals shows promise for guiding muscle functional training.
  • This approach may provide objective indicators for optimizing training intensity and amplitude.
  • Improved training guidance can lead to better clinical outcomes for lip incompetence.