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Bodily expressed emotion understanding through integrating Laban movement analysis.

Chenyan Wu1, Dolzodmaa Davaasuren1, Tal Shafir2

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This study introduces a new dataset and method to improve automatic recognition of emotions from body movements. Utilizing human motor elements enhances bodily expressed emotion understanding (BEEU) for better mental health analysis.

Keywords:
affective computingcomputer visiondancedeep learningemotion recognitionperforming artspsychologyroboticsvideo understanding

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Area of Science:

  • Computer Science
  • Psychology
  • Affective Computing

Background:

  • Bodily expressed emotion understanding (BEEU) aims to recognize emotions from human movement.
  • Psychological studies confirm that specific motor elements convey emotions.
  • Integrating motor elements can enhance automated emotion recognition systems.

Purpose of the Study:

  • To develop a novel approach for BEEU by incorporating human motor elements.
  • To introduce a precise dataset of human motor elements for research.
  • To enhance BEEU models using a dual-source training strategy.

Main Methods:

  • Introduction of BoME (body motor elements), a novel dataset for human motor elements.
  • Application of baseline deep learning models to estimate motor elements from movement data.
  • Development of a dual-source solution training BEEU models with both motor element and emotion labels.

Main Results:

  • Deep learning models effectively learn representations of human movement from the BoME dataset.
  • The proposed dual-source solution significantly improves BEEU performance on the BoLD benchmark.
  • Experimental results demonstrate the benefit of integrating motor elements for emotion recognition.

Conclusions:

  • Human motor elements are crucial for accurate bodily expressed emotion understanding.
  • The BoME dataset and dual-source approach offer a promising direction for BEEU research.
  • This work has implications for advancing emotion understanding and mental health analysis tools.