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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Recognition of Microexpressions Using Nonlinearly Transformed Zygomaticus Muscle Activation.

Sarva Kiruthika Aruldass1, Karthick P A2, Ramakrishnan Swaminathan1

  • 1Non-Invasive Imaging and Diagnostics Lab, Department of Applied Mechanics and Biomedical Engineering, IIT Madras, Chennai, India.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary

This study enhances emotion classification using facial Electromyography (fEMG) signals. Muscle activation techniques improved happy and sad Micro-Expression (ME) detection accuracy to 91.6% with Random Forest classifiers.

Keywords:
ElectromyographyMicro-ExpressionsMuscle activation

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Area of Science:

  • Biomedical Engineering
  • Affective Computing
  • Signal Processing

Background:

  • Facial Electromyography (fEMG) is crucial for analyzing subtle facial muscle movements.
  • Micro-Expressions (MEs) convey emotional states, but accurate classification remains challenging.
  • Nonlinear dynamics in biological signals offer potential for improved analysis.

Purpose of the Study:

  • To classify happy and sad Micro-Expressions (MEs) using facial Electromyography (fEMG) signals.
  • To investigate the utility of a muscle activation technique for ME analysis.
  • To compare classification accuracy using muscle activation features versus normalized fEMG signals.

Main Methods:

  • Recorded fEMG signals from the zygomaticus region during ME elicitation.
  • Applied a muscle activation technique to model nonlinear EMG-force relationships.
  • Extracted Time-Domain (TD) features from muscle activation signals.
  • Utilized a Random Forest (RF) classifier for ME classification.

Main Results:

  • The muscle activation technique, analyzing nonlinear shape factor A values, proved effective.
  • TD features from muscle activation signals yielded the highest classification accuracy of 91.6%.
  • Classification accuracy decreased when features were derived from normalized fEMG signals.

Conclusions:

  • Muscle activation analysis significantly enhances the accuracy of emotion classification from fEMG signals.
  • The proposed method offers a robust approach for Micro-Expression recognition.
  • This technique holds promise for applications in human-computer interaction and mental health monitoring.