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Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
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Embodied Emotion Recognition Based on Life-Logging.

Ayoung Cho1, Hyunwoo Lee1, Youngho Jo2

  • 1Department of Emotion Engineering, University of Sangmyung, Seoul 03016, Korea.

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|December 8, 2019
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Summary

This study introduces a novel method for recognizing embodied emotion by analyzing the interplay between physiological responses, behavior, and environment using life-logging data. It reveals how these interconnected factors significantly influence emotional states like arousal and valence.

Keywords:
ambient noisecausalityembodied emotionglobal positioning system (GPS)life-loggingphotoplethysmogram (PPG)

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Area of Science:

  • Psychology
  • Computer Science
  • Human-Computer Interaction

Background:

  • Embodied emotion arises from complex interactions between physiological, behavioral, and environmental factors.
  • Existing emotion recognition methods often overlook the interconnectedness of these variables, focusing on isolated data points.
  • A comprehensive approach is needed to understand how these elements dynamically influence emotional states.

Purpose of the Study:

  • To develop and validate a novel method for determining embodied emotion by integrating physiological, behavioral, and environmental data.
  • To investigate the inter-connectivity among these factors in real-life settings using life-logging.
  • To analyze how these interactions influence emotional arousal and valence levels.

Main Methods:

  • Collected physiological data (heart rate variability - HRV), behavioral data (Global Positioning System - GPS spatiotemporal features), and environmental data (ambient noise) from 79 participants over two weeks.
  • Mapped collected life-logging data with self-reported emotion levels (arousal and valence) on a seven-point scale.
  • Employed multiple regression analysis to examine the relationships and interactions among the collected data and emotional states.

Main Results:

  • Significant differences in emotional arousal and valence levels were observed, demonstrating the impact of the interplay between physiological, behavioral, and environmental factors.
  • The study successfully identified specific interactions that significantly contribute to determining arousal and valence levels.
  • Individual differences in how these factors interact to shape emotion were highlighted.

Conclusions:

  • Emotion recognition in real-life settings is feasible and practical through integrated life-logging data analysis.
  • The study confirms that embodied emotion is dynamically influenced by the interaction of physiological, behavioral, and environmental factors, a crucial aspect previously overlooked.
  • This research provides a foundation for more holistic and accurate emotion recognition systems.