Fractal Analysis of Electrodermal Activity for Emotion Recognition: A Novel Approach Using Detrended Fluctuation Analysis and Wavelet Entropy DOI Creative Commons
Luís Roberto Mercado Díaz, Yedukondala Rao Veeranki, Edward W. Large

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(24), P. 8130 - 8130

Published: Dec. 19, 2024

The field of emotion recognition from physiological signals is a growing area research with significant implications for both mental health monitoring and human–computer interaction. This study introduces novel approach to detecting emotional states based on fractal analysis electrodermal activity (EDA) signals. We employed detrended fluctuation (DFA), Hurst exponent estimation, wavelet entropy calculation extract features EDA obtained the CASE dataset, which contains recordings continuous annotations 30 participants. revealed differences in across five (neutral, amused, bored, relaxed, scared), particularly those derived entropy. A cross-correlation showed robust correlations between arousal valence dimensions emotion, challenging conventional view as predominantly arousal-indicating measure. application machine learning classification using achieved leave-one-subject-out accuracy 84.3% an F1 score 0.802, surpassing performance previous methods same dataset. demonstrates potential capturing intricate, multi-scale dynamics recognition, opening new avenues advancing emotion-aware systems affective computing applications.

Language: Английский

An emotion recognition method based on frequency-domain features of PPG DOI Creative Commons

Zhibin Zhu,

Xuanyi Wang, Yifei Xu

et al.

Frontiers in Physiology, Journal Year: 2025, Volume and Issue: 16

Published: Feb. 25, 2025

This study aims to employ physiological model simulation systematically analyze the frequency-domain components of PPG signals and extract their key features. The efficacy these features in effectively distinguishing emotional states will also be investigated. A dual windkessel was employed signal frequency distinctive Experimental data collection encompassed both (PPG) psychological measurements, with subsequent analysis involving distribution patterns statistical testing (U-tests) examine feature-emotion relationships. implemented support vector machine (SVM) classification evaluate feature effectiveness, complemented by comparative using pulse rate variability (PRV) features, morphological DEAP dataset. results demonstrate significant differentiation responses arousal valence variations, achieving accuracies 87.5% 81.4%, respectively. Validation on dataset yielded consistent 73.5% (arousal) 71.5% (valence). Feature fusion incorporating proposed enhanced performance, surpassing 90% accuracy. uses modeling We effectiveness emotion recognition reveal relationships among parameters, states. These findings advance understanding mechanisms provide a foundation for future research.

Language: Английский

Citations

0
A Review of Machine Learning-Based Assessment of Depression DOI
Zhao Wang, Ziyi Cai,

Shuya Dong

et al.

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 266 - 290

Published: Jan. 1, 2025

Language: Английский

Citations

0
Emotion recognition via affective EEG signals: State of the art DOI
Wei Meng, Fazheng Hou, Mengyuan Zhao

et al.

Neurocomputing, Journal Year: 2025, Volume and Issue: unknown, P. 130418 - 130418

Published: May 1, 2025

Language: Английский

Citations

0
Systematic mapping study of tools to identify emotions and personality traits DOI Creative Commons
Amanul Islam, Nurul Fazmidar Mohd Noor,

Siti Soraya Abdul Rahman

et al.

Discover Artificial Intelligence, Journal Year: 2025, Volume and Issue: 5(1)

Published: May 16, 2025

Language: Английский

Citations

0
Impact of AI-Powered Adaptive Learning Platforms on English Reading Proficiency: Evidence from Structural Equation Modeling DOI Creative Commons
Jin Wu, Yiyun Wang, Fang Chen

et al.

IEEE Access, Journal Year: 2025, Volume and Issue: 13, P. 88230 - 88242

Published: Jan. 1, 2025

Language: Английский

Citations

0
Fractal Analysis of Electrodermal Activity for Emotion Recognition: A Novel Approach Using Detrended Fluctuation Analysis and Wavelet Entropy DOI Creative Commons
Luís Roberto Mercado Díaz, Yedukondala Rao Veeranki, Edward W. Large

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(24), P. 8130 - 8130

Published: Dec. 19, 2024

The field of emotion recognition from physiological signals is a growing area research with significant implications for both mental health monitoring and human–computer interaction. This study introduces novel approach to detecting emotional states based on fractal analysis electrodermal activity (EDA) signals. We employed detrended fluctuation (DFA), Hurst exponent estimation, wavelet entropy calculation extract features EDA obtained the CASE dataset, which contains recordings continuous annotations 30 participants. revealed differences in across five (neutral, amused, bored, relaxed, scared), particularly those derived entropy. A cross-correlation showed robust correlations between arousal valence dimensions emotion, challenging conventional view as predominantly arousal-indicating measure. application machine learning classification using achieved leave-one-subject-out accuracy 84.3% an F1 score 0.802, surpassing performance previous methods same dataset. demonstrates potential capturing intricate, multi-scale dynamics recognition, opening new avenues advancing emotion-aware systems affective computing applications.

Language: Английский

Citations

0