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DC Field | Value | Language |
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dc.contributor.author | Joo, Jae Hong | - |
dc.contributor.author | Han, Seung Hyun | - |
dc.contributor.author | Park, Inyoung | - |
dc.contributor.author | Chung, Tae Sun | - |
dc.date.issued | 2024-04-01 | - |
dc.identifier.issn | 2079-9292 | - |
dc.identifier.uri | https://aurora.ajou.ac.kr/handle/2018.oak/34154 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85191322488&origin=inward | - |
dc.description.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. | - |
dc.description.sponsorship | 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). | - |
dc.language.iso | eng | - |
dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | - |
dc.title | Immersive Emotion Analysis in VR Environments: A Sensor-Based Approach to Prevent Distortion | - |
dc.type | Article | - |
dc.citation.number | 8 | - |
dc.citation.title | Electronics (Switzerland) | - |
dc.citation.volume | 13 | - |
dc.identifier.bibliographicCitation | Electronics (Switzerland), Vol.13 No.8 | - |
dc.identifier.doi | 10.3390/electronics13081494 | - |
dc.identifier.scopusid | 2-s2.0-85191322488 | - |
dc.identifier.url | www.mdpi.com/journal/electronics | - |
dc.subject.keyword | emotion analysis | - |
dc.subject.keyword | emotional dimension model | - |
dc.subject.keyword | immersion | - |
dc.subject.keyword | sensor data | - |
dc.subject.keyword | virtual reality | - |
dc.type.other | Article | - |
dc.description.isoa | true | - |
dc.subject.subarea | Control and Systems Engineering | - |
dc.subject.subarea | Signal Processing | - |
dc.subject.subarea | Hardware and Architecture | - |
dc.subject.subarea | Computer Networks and Communications | - |
dc.subject.subarea | Electrical and Electronic Engineering | - |
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