标题： 情感和唤醒推断的数据集：综述 显示英文标题

标题： Datasets for Valence and Arousal Inference: A Survey

摘要： 理解人类情感可以用于机器人技术、市场营销、教育、人机交互、医疗保健、娱乐、自动驾驶和心理学，以提高决策能力、个性化体验和改善情绪健康。 这项工作全面概述了利用连续效价和唤醒度标签的情感推断数据集。 我们回顾了2008年至2024年间发布的25个数据集，考察了数据集规模、受试者分布、传感器配置、标注尺度和效价与唤醒度值的数据格式等关键因素。 虽然基于摄像头的数据集在该领域占主导地位，但我们还识别出几种广泛使用的多模态组合。 此外，我们探讨了应用于这些数据集的最常见的情感检测方法，提供了对该领域主流方法的见解。 我们对传感器融合方法的综述显示，在效价和唤醒度推断模型改进方面取得了有前景的进展。 显示英文摘要

摘要： 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.