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Fear Level Classification Based on Emotional Dimensions and Machine Learning Techniques.

Oana Bălan1, Gabriela Moise2, Alin Moldoveanu3

  • 1Department of Computer Science and Engineering, Faculty of Automatic Control and Computers, University POLITEHNICA of Bucharest, 060042 Bucharest, Romania. oana.balan@cs.pub.ro.

Sensors (Basel, Switzerland)
|April 14, 2019
PubMed
Summary
This summary is machine-generated.

This study compared machine learning models for classifying fear levels using physiological data. The Random Forest Classifier achieved the highest accuracy in identifying fear states for phobia treatment.

Keywords:
affective computingemotional assessmentfear classificationfeature selection

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Area of Science:

  • Affective computing
  • Artificial intelligence in emotion modeling

Background:

  • Affective computing has advanced significantly, integrating AI for emotion modeling.
  • Developing systems for phobia treatment requires accurate emotion recognition.

Purpose of the Study:

  • To compare machine and deep learning techniques for fear level classification.
  • To develop an adaptive system for phobia treatment based on user's affective state.

Main Methods:

  • Utilized electroencephalogram (EEG) and peripheral data from the DEAP database.
  • Compared Random Forest, SVM, k-NN, DNNs, and other models with and without feature selection.
  • Employed two-level (fear/no fear) and four-level (no fear to high fear) fear paradigms.

Main Results:

  • All tested methods showed good classification accuracy.
  • The Random Forest Classifier yielded the highest F scores: 89.96% (two-level) and 85.33% (four-level).

Conclusions:

  • Random Forest Classifier demonstrates superior performance in fear level recognition.
  • This research supports the development of AI-driven adaptive phobia treatment systems.