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Immersive Emotion Analysis in VR Environments: A Sensor-Based Approach to Prevent Distortionoa mark
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Publication Year
2024-04-01
Journal
Electronics (Switzerland)
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Citation
Electronics (Switzerland), Vol.13 No.8
Keyword
emotion analysisemotional dimension modelimmersionsensor datavirtual reality
All Science Classification Codes (ASJC)
Control and Systems EngineeringSignal ProcessingHardware and ArchitectureComputer Networks and CommunicationsElectrical and Electronic Engineering
Abstract
As virtual reality (VR) technology advances, research has focused on enhancing VR content for a more realistic user experience. Traditional emotion analysis relies on surveys, but they suffer from delayed responses and decreased immersion, leading to distorted results. To overcome these limitations, we propose an emotion analysis method using sensor data in the VR environment. Our approach can take advantage of the user’s immediate response and not reduce immersion. Linear regression, classification analysis, and tree-based methods were applied to electrocardiogram and galvanic skin response (GSR) sensor data to measure valence and arousal values. We introduced a novel emotional dimension model by analyzing correlations between emotions and the valence and arousal values. Experimental results demonstrated the highest accuracy of 77% and 92.3% for valence and arousal prediction, respectively, using GSR sensor data. Furthermore, an accuracy of 80.25% was achieved in predicting valence and arousal using nine emotions. Our proposed model improves VR content through more accurate emotion analysis in a VR environment, which can be useful for targeting customers in various industries, such as marketing, gaming, education, and healthcare.
ISSN
2079-9292
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/34154
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85191322488&origin=inward
DOI
https://doi.org/10.3390/electronics13081494
Journal URL
www.mdpi.com/journal/electronics
Type
Article
Funding
This work was supported by Institute of Information & communications Technology Planning & Evaluation (IITP) under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2024-RS-2023-00255968) grant and the ITRC (Information Technology Research Center) support program (IITP-2021-0-02051) funded by the Korea government (MSIT). This work was supported by a research grant from Seoul Women\\u2019s University (2024-0031).
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