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Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
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Facial electromyogram-based emotion recognition for virtual reality applications using machine learning classifiers

Jung-Hwan Kim1, Ho-Seung Cha2,3, Chang-Hwan Im1,3

  • 1Department of Artificial Intelligence, Hanyang University, Seoul, 04763 Republic of Korea.

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|July 8, 2025
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Summary

This study introduces a faster method for emotion recognition in virtual reality (VR) using facial electromyography (fEMG) and posed expressions, bypassing lengthy calibration for more accessible VR experiences.

Keywords:
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Area of Science:

  • Human-Computer Interaction
  • Affective Computing
  • Virtual Reality

Background:

  • Camera-based emotion recognition in VR is limited by head-mounted displays (HMDs).
  • Facial electromyography (fEMG) is a viable alternative for VR emotion recognition.
  • Current fEMG methods require extensive calibration, hindering user adoption.

Purpose of the Study:

  • To develop a simplified calibration method for fEMG-based emotion recognition in VR.
  • To enable efficient and user-friendly emotion detection within virtual reality environments.

Main Methods:

  • Trained a machine learning classifier using fEMG data from posed facial expressions.
  • Validated the model with 20 participants using posed expressions for calibration and videos for testing.
  • Focused on classifying high- and low-valence emotional states.

Main Results:

  • Achieved a macro F1-score of 88.20% for classifying valence states.
  • Demonstrated the effectiveness and efficiency of the posed expression calibration method.
  • Successfully built the first fEMG-based emotion recognition model using posed facial expressions.

Conclusions:

  • The proposed method significantly simplifies fEMG calibration for VR emotion recognition.
  • This approach enhances the practicality and efficiency of emotion detection in immersive environments.
  • Paves the way for user-friendly interfaces in VR.