构建首个融合身体动作与生理信号的虚拟现实情绪数据集。
MVRS: The Multimodal Virtual Reality Stimuli-based Emotion Recognition Dataset
- 用VR刺激13人，同步采集眼动、体动、肌电和皮肤电
- 多模态数据时间对齐，支持早期与晚期融合分析
- 适合情绪计算、人机交互研究者使用
随着人工智能的发展，自动情绪识别在医疗、教育和车载系统中日益重要。然而，缺乏包含身体运动和生理信号的多模态数据集，限制了该领域进展。为此，本文提出MVRS数据集，收录13名年龄12至60岁参与者在虚拟现实情绪刺激（放松、恐惧、压力、悲伤、喜悦）下的同步数据，通过头戴式摄像头采集眼动，使用Kinect v2获取体动，利用Arduino UNO记录肌电（EMG）和皮肤电导（GSR），所有信号均时间对齐。参与者遵循统一协议并签署知情同意书，完成问卷调查。从各模态提取特征，采用早期和晚期融合方法进行分类评估，验证了数据质量与情绪可分性，为多模态情感计算提供重要资源。
原文摘要 · Abstract (English)
Automatic emotion recognition has become increasingly important with the rise of AI, especially in fields like healthcare, education, and automotive systems. However, there is a lack of multimodal datasets, particularly involving body motion and physiological signals, which limits progress in the field. To address this, the MVRS dataset is introduced, featuring synchronized recordings from 13 participants aged 12 to 60 exposed to VR based emotional stimuli (relaxation, fear, stress, sadness, joy). Data were collected using eye tracking (via webcam in a VR headset), body motion (Kinect v2), and EMG and GSR signals (Arduino UNO), all timestamp aligned. Participants followed a unified protocol with consent and questionnaires. Features from each modality were extracted, fused using early and late fusion techniques, and evaluated with classifiers to confirm the datasets quality and emotion separability, making MVRS a valuable contribution to multimodal affective computing.
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