{"title":"基于虚拟现实和动态决策融合的恐惧心理弹性训练研究","authors":"Yangzhao Yu, Bin He, Guangjie Yu, Faxin Zhong","doi":"10.1109/PRMVIA58252.2023.00045","DOIUrl":null,"url":null,"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.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on fear mental resilience training based on virtual reality and dynamic decision fusion\",\"authors\":\"Yangzhao Yu, Bin He, Guangjie Yu, Faxin Zhong\",\"doi\":\"10.1109/PRMVIA58252.2023.00045\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":221346,\"journal\":{\"name\":\"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PRMVIA58252.2023.00045\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PRMVIA58252.2023.00045","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on fear mental resilience training based on virtual reality and dynamic decision fusion
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.