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Related Concept Videos

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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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Stanley Schachter and Jerome Singer proposed the two-factor theory of emotion, which emphasizes the interplay between physiological arousal and cognitive labeling in forming emotional experiences. This theory suggests that emotions are not simply a result of physiological responses but rather a combination of these responses and the individual's cognitive interpretation of them.
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Related Experiment Video

Updated: Aug 7, 2025

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
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Emotion Recognition Using Different Sensors, Emotion Models, Methods and Datasets: A Comprehensive Review.

Yujian Cai1, Xingguang Li1, Jinsong Li1

  • 1School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China.

Sensors (Basel, Switzerland)
|March 11, 2023
PubMed
Summary

This survey reviews over 200 papers on machine emotion recognition using various sensors. It compares unimodal and multimodal approaches, highlighting sensor advantages and disadvantages for affective computing.

Keywords:
classifiersemotion modelsemotion recognition datasetsemotional signal processingsensors for emotion recognition

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Machine-based human emotion recognition is advancing due to sensor and IT development.
  • Emotion recognition is crucial for affective computing, utilizing facial expressions, speech, behavior, and physiological signals.
  • Existing surveys often focus on single sensors, necessitating a comparative analysis of unimodal and multimodal systems.

Purpose of the Study:

  • To comprehensively review and categorize over 200 papers on emotion recognition.
  • To compare different sensors, methods, and datasets used in emotion recognition systems.
  • To analyze the advantages and disadvantages of various sensors for emotion recognition.

Main Methods:

  • Literature research was employed to collect and analyze relevant papers.
  • Papers were categorized based on their innovative contributions.
  • Focus was placed on methods, datasets, and sensor modalities for emotion recognition.

Main Results:

  • A categorization of emotion recognition papers based on innovations, sensors, methods, and datasets.
  • A comparative analysis of different sensors, including their strengths and weaknesses.
  • Identification of application examples and developmental trends in emotion recognition.

Conclusions:

  • This survey provides a consolidated overview of the emotion recognition landscape.
  • It facilitates researchers in selecting appropriate sensors, algorithms, and datasets.
  • The findings support the advancement of affective computing through informed system design.