自动化与仪器仪表2024,Issue(8) :113-116.DOI:10.14016/j.cnki.1001-9227.2024.08.113

面向心理健康监测的多视角机制与虚拟现实融合技术研究

Research on Multi perspective Mechanism and Virtual Reality Fusion Technology for Mental Health Monitoring

田育娟 田花妮
自动化与仪器仪表2024,Issue(8) :113-116.DOI:10.14016/j.cnki.1001-9227.2024.08.113

面向心理健康监测的多视角机制与虚拟现实融合技术研究

Research on Multi perspective Mechanism and Virtual Reality Fusion Technology for Mental Health Monitoring

田育娟 1田花妮2
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作者信息

  • 1. 西安思源学院,西安 710038
  • 2. 爱立信(西安)信息技术服务有限公司,西安 710065
  • 折叠

摘要

针对传统情绪诱导材料效率低、敏感度高的问题,研究利用虚拟现实技术建立情绪诱导系统,并采用双监督神经网络模型进行情绪识别,建立分类与回归双损失函数约束的情绪分类算法.在此基础上引入多视角机制,实现自动标注,提高情绪识别的准确性和效率.结果显示,童话乐园场景正面情绪置信度的时间占比达到了 85.75%,幽深山洞场景负面情绪置信区间的时间占比高达95%,风景田园和海边落日场景中立情绪置信度的时间占比分别为90.68%和88.76%,证明了面向心理健康监测的多视角机制与虚拟现实融合技术的有效性.

Abstract

In response to the problems of low efficiency and high sensitivity of traditional emotion inducing materials,this study investigates the use of virtual reality technology to establish an emotion inducing system,and uses a dual supervised neural network model for emotion recognition,building an emotion classification technique using a classification algorithm and regression dual loss function constraints.On this basis,a multi perspective mechanism is introduced to achieve automatic annotation and improve the ac-curacy and efficiency of emotion recognition.The results showed that the time proportion of positive emotional confidence in fairy tale park scenes reached 85.75%,the time proportion of negative emotional confidence interval in deep cave scenes reached 95%,and the time proportion of neutral emotional confidence in scenic countryside and seaside sunset scenes was 90.68%and 88.76%,respec-tively.This confirms the effectiveness of multi perspective mechanisms for mental health monitoring and virtual reality integration technology.

关键词

虚拟现实技术/双监督神经网络/情绪分类算法/多视角机制

Key words

virtual reality technology/double supervised neural network/emotion classification algorithm/multi perspective mechanism

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基金项目

陕西省"十四五"教育科学规划2023年度课题()

出版年

2024
自动化与仪器仪表
重庆工业自动化仪表研究所,重庆市自动化与仪器仪表学会

自动化与仪器仪表

CSTPCD
影响因子:0.327
ISSN:1001-9227
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