MVRS: The Multimodal Virtual Reality Stimuli-based Emotion Recognition Dataset
This dataset addresses a gap in multimodal affective computing for researchers, but it is incremental as it primarily provides new data rather than novel methods.
The authors tackled the lack of multimodal datasets for emotion recognition by introducing the MVRS dataset, which includes synchronized recordings from 13 participants using VR stimuli and multiple modalities, and they confirmed its quality through feature extraction and classification, showing emotion separability.
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.