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VRMN-bD: A Multi-mo...
VRMN-bD: A Multi-modal Natural Behavior Dataset of Immersive Human Fear Responses in VR Stand-up Interactive Games
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- Zhang, He (author)
- Tsinghua University, The Future Laboratory, Tsinghua University, The Future Laboratory; Penn State University, College of Information Sciences and Technology, Penn State University, College of Information Sciences and Technology
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- Li, Xinyang (author)
- Tsinghua University, Academy of Arts & Design, Tsinghua University, Academy of Arts & Design
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- Sun, Yuanxi (author)
- Communication University of China, School of Computer and Cyber Sciences, Communication University of China, School of Computer and Cyber Sciences
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- Fu, Xinyi (author)
- Tsinghua University, The Future Laboratory, Tsinghua University, The Future Laboratory
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- Qiu, Christine (author)
- KTH,Medieteknik och interaktionsdesign, MID
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- Carroll, John M. (author)
- Penn State University, College of Information Sciences and Technology, Penn State University, College of Information Sciences and Technology
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2024
- 2024
- English.
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In: Proceedings - 2024 IEEE Conference on Virtual Reality and 3D User Interfaces, VR 2024. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 320-330
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- Understanding and recognizing emotions are important and challenging issues in the metaverse era. Understanding, identifying, and predicting fear, which is one of the fundamental human emotions, in virtual reality (VR) environments plays an essential role in immersive game development, scene development, and next-generation virtual human-computer interaction applications. In this article, we used VR horror games as a medium to analyze fear emotions by collecting multi-modal data (posture, audio, and physiological signals) from 23 players. We used an LSTM-based model to predict fear with accuracies of 65.31% and 90.47% under 6-level classification (no fear and five different levels of fear) and 2-level classification (no fear and fear), respectively. We constructed a multi-modal natural behavior dataset of immersive human fear responses (VRMN-bD) and compared it with existing relevant advanced datasets. The results show that our dataset has fewer limitations in terms of collection method, data scale and audience scope. We are unique and advanced in targeting multi-modal datasets of fear and behavior in VR stand-up interactive environments. Moreover, we discussed the implications of this work for communities and applications. The dataset and pre-trained model are available at https://github.com/KindOPSTAR/VRMN-bD.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences (hsv//eng)
Keyword
- Activity recognition and understanding
- Artificial intelligence
- Computing methodologies
- Database
- HCI design and evaluation methods
- Human computer interaction (HCI)
- Human-centered computing
- Virtual reality
Publication and Content Type
- ref (subject category)
- kon (subject category)
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