ArticleScientific data2024
A real-world dataset of group emotion experiences based on physiological data.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A large scale dataset of emotion annotated physiological signals collected from a public exhibit.Scientific reports · 2026Article
- A novel multi-module neural networks strategy of human emotion recognition in the human-robot interaction.Scientific reports · 2026Article
- LLaMAC: low-cost biosignal sensor based large multimodal dataset for affective computing.Scientific data · 2025Article
- Article
- A Multimodal Dataset for Mixed Emotion Recognition.Scientific data · 2024Article
- A real-world dataset of group emotion experiences based on physiological data.Scientific data · 2024Article
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5 authors.
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Abstract
Affective computing has experienced substantial advancements in recognizing emotions through image and facial expression analysis. However, the incorporation of physiological data remains constrained. Emotion recognition with physiological data shows promising results in controlled experiments but lacks generalization to real-world settings. To address this, we present G-REx, a dataset for real-world affective computing. We collected physiological data (photoplethysmography and electrodermal activity) using a wrist-worn device during long-duration movie sessions. Emotion annotations were retrospectively performed on segments with elevated physiological responses. The dataset includes over 31 movie sessions, totaling 380 h+ of data from 190+ subjects. The data were collected in a group setting, which can give further context to emotion recognition systems. Our setup aims to be easily replicable in any real-life scenario, facilitating the collection of large datasets for novel affective computing systems.
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