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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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A Mobile Game for Automatic Emotion-Labeling of Images.

Haik Kalantarian1, Khaled Jedoui2, Peter Washington3

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This study introduces a new method for training emotion classifiers for children with Autism Spectrum Disorder (ASD) using game data. The technique significantly improves the accuracy of identifying emotions during social training games.

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Area of Science:

  • Human-computer interaction
  • Developmental psychology
  • Machine learning for healthcare

Background:

  • Social training for children with Autism Spectrum Disorder (ASD) is crucial for development.
  • Mobile games offer a promising platform for delivering social training interventions.
  • Existing emotion recognition systems struggle with accuracy for children with ASD.

Purpose of the Study:

  • To address limitations in current emotion recognition for children with ASD in a game-based intervention.
  • To propose and evaluate a novel technique for automatically extracting emotion-labeled data from game sessions.
  • To improve the development of adaptive, real-time feedback systems for social training games.

Main Methods:

  • Development of a mobile charades-style game for social training.
  • Integration of emotion classifiers requiring real-time performance feedback.
  • Proposal of a novel technique using probability scores and meta-information to extract emotion-labeled frames from game videos.
  • Evaluation against existing emotion classification APIs.

Main Results:

  • The proposed technique achieved 83% accuracy in identifying emotion-labeled frames.
  • This significantly outperforms the baseline accuracy of 51.6% from the best-performing commercial API.
  • The method demonstrates potential for creating more effective emotion classifiers for children with ASD.

Conclusions:

  • The novel frame extraction technique shows high efficacy in generating training data for emotion classifiers.
  • This approach can overcome the limitations of current emotion recognition platforms for children with ASD.
  • The developed method facilitates the creation of more responsive and personalized social training games.