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Labeling Emotion01:20

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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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The physiology of emotions is a multifaceted process involving the autonomic nervous system, brain structures, hormones, and neurotransmitters. This intricate interplay dictates how emotions manifest in the body and influence behavior.
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Emotional expression encompasses how individuals convey their emotions through verbal communication and non-verbal cues. These non-verbal actions include facial expressions, body language, and physical gestures, such as frowning or smiling. Among these, facial expressions play a crucial role in emotional expression and are understood universally, indicating a biological basis for how humans communicate emotions.
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Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
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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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Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
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Late Fusion Model for Emotion Recognition from Facial Expressions and Biosignals in a Dataset of Children with Autism Spectrum Disorder.

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Related Experiment Video

Updated: Oct 30, 2025

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
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Graph Representation Integrating Signals for Emotion Recognition and Analysis.

Teresa Zawadzka1, Tomasz Wierciński1, Grzegorz Meller1

  • 1Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, 80-233 Gdańsk, Poland.

Sensors (Basel, Switzerland)
|July 2, 2021
PubMed
Summary

This study introduces the Graph Representation Integrating Signals for Emotion Recognition and Analysis (GRISERA) framework. GRISERA enables consistent integration and retrieval of multimodal data for Affective Computing research, enhancing data reusability.

Keywords:
affective computingbiosignalsdatasetsemotion recognitiongraph databasessignal integration

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Area of Science:

  • Affective Computing
  • Data Science
  • Signal Processing

Background:

  • Data reusability is crucial in scientific research.
  • Affective Computing relies on diverse datasets (biosignals, video, images).
  • Creating large datasets for Affective Computing is costly and time-consuming.

Purpose of the Study:

  • To present a novel framework for integrating and storing multimodal experimental data.
  • To address the challenge of data reusability in Affective Computing.
  • To enable efficient data retrieval for analysis and model training.

Main Methods:

  • Development of the Graph Representation Integrating Signals for Emotion Recognition and Analysis (GRISERA) framework.
  • Utilizing a standardized graph model for data representation and storage.
  • Implementing methods for data integration and query pattern creation.

Main Results:

  • Demonstrated successful storage and retrieval of data from the AMIGOS dataset for deep learning.
  • Validated the integration of signals from multiple sources (AMIGOS, ASCERTAIN, DEAP) within GRISERA.
  • Showcased the framework's capability for subsequent statistical analysis.

Conclusions:

  • GRISERA offers a persistent and consistent model for integrated signals in Affective Computing.
  • The framework facilitates data reusability across different experiments and datasets.
  • GRISERA is the first approach to address multi-experiment data integration and retrieval in this field.