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関連する概念動画

Physiology of Emotion01:20

Physiology of Emotion

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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.
Autonomic Nervous System
The autonomic nervous system (ANS) plays a critical role in emotional responses by regulating involuntary physiological functions. It consists of two main components: the sympathetic and parasympathetic systems. The sympathetic system...
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Emotional Expression01:26

Emotional Expression

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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.
Universal Facial Expressions
Psychologist Paul Ekman identified seven basic...
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ECG Interpretation of Rhythms01:24

ECG Interpretation of Rhythms

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An electrocardiogram (ECG)graphically represents the heart's electrical activity on ECG paper or a monitor.
Components of the Electrocardiogram
The primary components of a normal ECG waveform in Normal sinus rhythm(NSR) include the P wave, PR interval, QRS complex, ST segment, T wave, and occasionally a U wave.
ECG waveforms are divided by vertical and horizontal lines at standard intervals.
The horizontal axis measures time and rate, and the vertical axis measures amplitude or voltage....
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Labeling Emotion01:20

Labeling Emotion

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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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Correlation between ECG and Cardiac Cycle01:25

Correlation between ECG and Cardiac Cycle

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The electrical signals recorded on an electrocardiogram (ECG) occur before the mechanical processes of contraction and relaxation during the cardiac cycle.
A cardiac action potential originates in the SA node and spreads throughout the atria and the AV node in approximately 0.03 seconds. This results in the P wave in an ECG and triggers atrial contraction. The action potential is then briefly slowed at the AV node, allowing the atria to contract and fill the ventricles with blood before...
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Introduction to Motivation and Emotion01:29

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Motivation is a multifaceted process that drives behavior toward fulfilling various physiological or psychological needs. This process involves initiating, guiding, and maintaining specific actions influenced by internal and external factors. For example, when someone feels hungry while watching television, hunger is a motivator, prompting the individual to get up, walk to the kitchen, and find something to eat. In this instance, hunger initiates and sustains the behavior necessary to meet the...
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関連する実験動画

Updated: Feb 13, 2026

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
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1D心電図からの2D特徴抽出に基づく感情認識

Siyi Wei1, Yukun An2, Jiaxue Chen1

  • 1School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210016.

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
|February 11, 2026
PubMed
まとめ

本研究は、心電図（ECG）信号を用いた客観的感情認識のための新しい方法を導入する。1D ECGデータを2D画像に変換することにより、このアプローチは精度向上のための時空間特徴抽出を強化する。

キーワード:
1D-2D信号変換深層学習感情認識ウェーブレットパケット分解

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科学分野:

  • 生体医工学
  • 人工知能
  • 信号処理

背景:

  • 客観的感情認識は、ヘルスケアや教育などの分野で重要である。
  • 心電図（ECG）信号は、感情検出のための実行可能でユーザーフレンドリーなバイオマーカーを提供する。
  • 深層学習を用いた1D心電図信号からの時空間特徴の抽出には課題が存在する。

研究 の 目的:

  • 心電図信号を用いた効果的な客観的感情認識方法を開発すること。
  • 深層学習における1D心電図データの処理の限界に対処すること。
  • 心電図信号からの時空間特徴の抽出と融合を強化すること。

主な方法:

  • ウェーブレットパケット分解を用いた1D-2D信号変換技術が採用された。
  • 一次元心電図信号は二次元画像表現に変換された。
  • Fusion Blockモジュールで拡張されたResNet18アーキテクチャが、2D心電図画像を処理した。

主要な成果:

  • 提案手法は感情認識タスクにおいて性能を向上させた。
  • 平均精度とF1スコアは、最適とは言えない手法と比較してそれぞれ2.19および4.48パーセンテージポイント向上した。
  • WESADおよびSWELL-KWデータセットで実験が実施された。

結論:

  • 新しい1D-2D ECG変換とResNet18ベースのアプローチは、客観的感情認識を大幅に改善する。
  • この方法は、高度な感情認識システムの開発のための技術的サポートを提供する。
  • 本研究の結果は、感情コンピューティングのためのバイオマーカーとしての心電図信号の可能性を強調している。