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.

翻译：理解与识别情绪是元宇宙时代重要且具有挑战性的课题。作为人类基本情绪之一，在虚拟现实环境中理解、识别和预测恐惧情绪，对沉浸式游戏开发、场景设计及下一代虚拟人机交互应用至关重要。本文以VR恐怖游戏为媒介，通过采集23名玩家的多模态数据（姿态、音频和生理信号）分析恐惧情绪。我们采用基于LSTM的模型进行恐惧预测，在6级分类（无恐惧及五种不同程度恐惧）和2级分类（无恐惧与恐惧）下分别达到65.31%和90.47%的准确率。我们构建了沉浸式人类恐惧反应多模态自然行为数据集（VRMN-bD），并与现有相关先进数据集进行对比。结果表明，本数据集在采集方法、数据规模及受众范围方面限制更少。我们在针对VR站立交互环境中恐惧与行为的多模态数据集方面具有独特性和先进性。此外，本文还讨论了该工作对社区和应用的启示。数据集与预训练模型可通过 https://github.com/KindOPSTAR/VRMN-bD 获取。