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Multi-physiological signal fusion for objective emotion recognition in educational human-computer interaction.

Wanmeng Wu1, Enling Zuo2, Weiya Zhang3

  • 1School of International Education and Exchange, Changchun Sci-Tech University, Changchun, China.

Frontiers in Public Health
|December 11, 2024
PubMed
Summary

This study developed a novel emotion recognition system using physiological signals to improve wellbeing for higher education teachers. The system accurately identifies emotional states, enhancing teacher-student interactions in educational settings.

Keywords:
artificial intelligenceeducational evaluationemotion recognitionhuman–computer interactionmulti-physiological signalswearable devices

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Area of Science:

  • Human-Computer Interaction
  • Affective Computing
  • Educational Technology

Background:

  • Rising psychological stress among higher education teachers requires innovative wellbeing solutions.
  • Emotion recognition technology in educational Human-Computer Interaction (HCI) systems presents a promising approach.
  • Enhancing teacher-student interactions is crucial for effective learning environments.

Purpose of the Study:

  • To develop a robust, multi-physiological signal-based emotion recognition system.
  • To improve the assessment of teacher emotional states within educational HCI.
  • To enhance teacher-student interactions and support teacher wellbeing.

Main Methods:

  • Utilized wearable devices to collect electrocardiography (ECG), electromyography (EMG), electrodermal activity, and respiratory signals.
  • Applied time-domain and time-frequency domain analysis for feature extraction, followed by feature selection.
  • Employed a convolutional neural network (CNN) with attention mechanisms for emotion classification.

Main Results:

  • The developed system achieved superior accuracy in emotion recognition compared to existing methods.
  • Attention mechanisms enhanced interpretability by identifying key physiological features for classification.
  • Demonstrated a standardized and accurate method for assessing teacher emotional states.

Conclusions:

  • The novel emotion recognition system offers significant advancements for educational HCI.
  • Real-time integration can improve teacher-student interactions and learning outcomes.
  • Future work should focus on system generalizability across diverse populations and settings.