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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.00738 |
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| _version_ | 1866916982457106432 |
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| author | Schneider, Helen Pavlitska, Svetlana Gremmelmaier, Helen Zöllner, J. Marius |
| author_facet | Schneider, Helen Pavlitska, Svetlana Gremmelmaier, Helen Zöllner, J. Marius |
| contents | Understanding human affect can be used in robotics, marketing, education, human-computer interaction, healthcare, entertainment, autonomous driving, and psychology to enhance decision-making, personalize experiences, and improve emotional well-being. This work presents a comprehensive overview of affect inference datasets that utilize continuous valence and arousal labels. We reviewed 25 datasets published between 2008 and 2024, examining key factors such as dataset size, subject distribution, sensor configurations, annotation scales, and data formats for valence and arousal values. While camera-based datasets dominate the field, we also identified several widely used multimodal combinations. Additionally, we explored the most common approaches to affect detection applied to these datasets, providing insights into the prevailing methodologies in the field. Our overview of sensor fusion approaches shows promising advancements in model improvement for valence and arousal inference. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_00738 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Datasets for Valence and Arousal Inference: A Survey Schneider, Helen Pavlitska, Svetlana Gremmelmaier, Helen Zöllner, J. Marius Human-Computer Interaction Understanding human affect can be used in robotics, marketing, education, human-computer interaction, healthcare, entertainment, autonomous driving, and psychology to enhance decision-making, personalize experiences, and improve emotional well-being. This work presents a comprehensive overview of affect inference datasets that utilize continuous valence and arousal labels. We reviewed 25 datasets published between 2008 and 2024, examining key factors such as dataset size, subject distribution, sensor configurations, annotation scales, and data formats for valence and arousal values. While camera-based datasets dominate the field, we also identified several widely used multimodal combinations. Additionally, we explored the most common approaches to affect detection applied to these datasets, providing insights into the prevailing methodologies in the field. Our overview of sensor fusion approaches shows promising advancements in model improvement for valence and arousal inference. |
| title | Datasets for Valence and Arousal Inference: A Survey |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2510.00738 |