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[Emotion Recognition Based on Multiple Physiological Signals].

Shali Chen1,2,3, Liuyi Zhang4, Feng Jiang1,2,3

  • 1College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027.

Zhongguo Yi Liao Qi Xie Za Zhi = Chinese Journal of Medical Instrumentation
|August 8, 2020
PubMed
Summary

This study introduces a novel emotion recognition model using physiological signals like skin temperature and respiration. The model effectively identifies emotions, achieving high accuracy in valence, arousal, and dominance detection.

Keywords:
emotion recognitionmultiple physiological signalssupport vector machine

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Area of Science:

  • Physiological computing
  • Affective computing
  • Biomedical signal processing

Background:

  • Emotions are physiological reactions detectable through biosignals.
  • Accurate emotion recognition is crucial for human-computer interaction and mental health monitoring.
  • Existing methods often rely on limited physiological data or complex feature extraction.

Purpose of the Study:

  • To propose and evaluate a novel emotion recognition model.
  • To investigate the effectiveness of combining multiple physiological signals for emotion detection.
  • To enhance the accuracy and robustness of emotion recognition systems.

Main Methods:

  • Feature extraction from photoplethysmography, galvanic skin response, respiration amplitude, and skin temperature.
  • Application of the Support Vector Machine-Recursive Feature Elimination-Correlation Bias Reduction (SVM-RFE-CBR) algorithm for feature selection.
  • Classification of emotions using Support Vector Machines (SVM).
  • Model validation on the publicly available DEAP dataset.

Main Results:

  • The developed model achieved notable accuracy rates: 73.5% for valence, 81.3% for arousal, and 76.1% for dominance.
  • The combination of diverse physiological signals significantly improved emotion recognition performance.
  • The SVM-RFE-CBR algorithm effectively identified the most relevant features for classification.

Conclusions:

  • Emotion recognition can be effectively achieved by integrating multiple physiological signals.
  • The proposed model demonstrates a promising approach for real-time emotion detection.
  • Further research can explore additional physiological markers and advanced machine learning techniques.