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Emotion recognition systems with electrodermal activity: From affective science to affective computing

Year: 2025

Type: article

Study designs: Systematic Review, Meta-Analysis

Abstract: Affective computing is an interdisciplinary field that aims to automatically recognize and interpret emotions. Recent research has focused on using physiological signals (e.g., electrodermal activity) to improve emotion recognition. However, the theoretical emotion models that underlie these systems have received comparatively little attention. We conducted a systematic review and meta-analysis on electrodermal-activity-based emotionrecognition systems. Our findings suggest that arousal prediction models outperform valence prediction models, supporting our preregistered hypothesis. This correlates with arousal’s association with autonomic nervous system activity and its direct link to electrodermal activity. We also observed a mismatch between the machinelearning approaches most often used—chiefly classification models—and the predominantly dimensional emotion frameworks adopted in the literature. Specifically, although dimensional affective models are increasingly popular, there has not been a parallel rise in regression models that would better reflect the continuous nature of the underlying data. We conclude that a comprehensive understanding of affective states requires consideration of both psychological and computational perspectives in affective computing research.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.18325342

Author: D'Amelio, Tomas Ariel

Institution: Centre de Recerca Matemàtica

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: green

Funder: European Commission

Award: G5206731826