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Updated: Dec 15, 2025

Using Facial Electromyography to Assess Facial Muscle Reactions to Experienced and Observed Affective Touch in Humans
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Developing a pain intensity prediction model using facial expression: A feasibility study with electromyography.

Riitta Mieronkoski1, Elise Syrjälä2, Mingzhe Jiang2

  • 1Department of Nursing Science, University of Turku, Turku, Finland.

Plos One
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Summary
This summary is machine-generated.

Surface electromyography effectively detects facial muscle activity linked to pain. This method shows promise for objective pain assessment in non-verbal patients, though further research is needed.

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Area of Science:

  • Biomedical Engineering
  • Pain Medicine
  • Clinical Monitoring

Background:

  • Accurate pain assessment is crucial, especially for patients unable to self-report.
  • Automatic detection of facial expressions of pain is a key area for improving patient monitoring.
  • Existing automated systems face challenges in reliably determining pain levels from facial cues.

Purpose of the Study:

  • To evaluate the feasibility of using surface electromyography (sEMG) to detect facial expressions of pain.
  • To develop and test a machine learning model for predicting pain intensity based on facial muscle activity.
  • To identify specific facial muscles and their activation patterns associated with induced pain.

Main Methods:

  • Surface electromyography (sEMG) was used to measure the activity of five facial muscles in 31 healthy volunteers.
  • Two types of gradually increasing experimental pain stimuli were applied.
  • Statistical analysis of sEMG data and supervised machine learning were employed for pain prediction.

Main Results:

  • Corrugator supercilii muscle activation strongly correlated with self-reported pain.
  • Levator labii superioris and orbicularis oculi showed significant activation increases at pain thresholds.
  • A prediction model using corrugator supercilii and levator labii superioris waveform length achieved a c-index of 0.64.

Conclusions:

  • Facial muscle activity, particularly eyebrow lowering, nose wrinkling, and upper lip raising, is detectable during pain.
  • The sEMG-based pain prediction model shows modest but statistically significant performance.
  • Further studies with larger sample sizes are recommended to enhance model accuracy and explore individual pain variability.