Research on fear mental resilience training based on virtual reality and dynamic decision fusion

Yangzhao Yu, Bin He, Guangjie Yu, Faxin Zhong
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Abstract

Effective resilience training can prevent early post-traumatic stress disorder, but limitations in emotion induction and recognition make it extremely challenging. Thus, this paper presents a fear mental resilience training that uses virtual reality exposure therapy and introduces two key techniques - construction of virtual scenarios and dynamic weighted decision fusion. Firstly, virtual reality (VR) is proposed to construct three disaster scenarios to induce different level of fear emotion and combining VR with stroop test to improve ecological validity. Then, three different weights are designed by analyzing the modal and cross-modal information to establish a fear emotion classification model based on dynamic weighted decision fusion. Finally, combining VR scenarios with exposure therapy to achieve progressive fear resilience training. And evaluate the training effect according to the individual’s emotional state and stroop performance level. The results demonstrate the designed VR scenarios can effectively induce fear, the proposed data fusion method realizes dynamic weighted fusion according to the weight design, effectively integrates multimodal data information, thereby improving the classification performance of the model. And the mental resilience training based on VR and dynamic weighted decision fusion methods is of great significance for enhancing the mental resilience of the subjects.
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基于虚拟现实和动态决策融合的恐惧心理弹性训练研究
有效的恢复力训练可以预防早期创伤后应激障碍,但情绪诱导和识别的局限性使其极具挑战性。为此,本文提出了一种基于虚拟现实暴露疗法的恐惧心理弹性训练方法,并介绍了虚拟场景构建和动态加权决策融合两项关键技术。首先,提出利用虚拟现实技术构建三种灾难场景,诱导不同程度的恐惧情绪,并将虚拟现实技术与stroop测试相结合,提高生态效度;然后,通过分析模态和跨模态信息,设计三种不同的权重,建立基于动态加权决策融合的恐惧情绪分类模型;最后,将VR场景与暴露疗法相结合,实现渐进式恐惧弹性训练。并根据个体的情绪状态和整体表现水平对训练效果进行评价。结果表明,所设计的虚拟现实场景能够有效诱导恐惧,所提出的数据融合方法根据权重设计实现动态加权融合,有效集成了多模态数据信息,从而提高了模型的分类性能。基于虚拟现实和动态加权决策融合方法的心理弹性训练对增强被试心理弹性具有重要意义。
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