VRMN-bD: A Multi-modal Natural Behavior Dataset of Immersive Human Fear
  Responses in VR Stand-up Interactive Games
He Zhang Xinyang Li Yuanxi Sun Xinyi Fu Christine Qiu John M. Carroll
Understanding and recognizing emotions are important and challenging issues
in the metaverse era. Understanding, identifying, and predicting fear, which is
one of the fundamental human emotions, in virtual reality (VR) environments
plays an essential role in immersive game development, scene development, and
next-generation virtual human-computer interaction applications. In this
article, we used VR horror games as a medium to analyze fear emotions by
collecting multi-modal data (posture, audio, and physiological signals) from 23
players. We used an LSTM-based model to predict fear with accuracies of 65.31%
and 90.47% under 6-level classification (no fear and five different levels of
fear) and 2-level classification (no fear and fear), respectively. We
constructed a multi-modal natural behavior dataset of immersive human fear
responses (VRMN-bD) and compared it with existing relevant advanced datasets.
The results show that our dataset has fewer limitations in terms of collection
method, data scale and audience scope. We are unique and advanced in targeting
multi-modal datasets of fear and behavior in VR stand-up interactive
environments. Moreover, we discussed the implications of this work for
communities and applications. The dataset and pre-trained model are available
at https://github.com/KindOPSTAR/VRMN-bD.