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VR-Wizard: Towards an Emotion-Adaptive Experience in VR

Published: 14 December 2021 Publication History

Abstract

In this research, we investigate the impact of real-time biofeedback-based emotion adaptive Virtual Reality (VR) environments on the immersiveness, game engagement, and flow state using physiological information such as Electroencephalogram (EEG), Electrodermal Activity (EDA), and Heart Rate Variability (HRV). For this, we designed VR-Wizard, a personalized emotion-adaptive VR game akin to a Harry Potter experience with an objective to collect items in the forbidden forest. The users initially train the system through a calibration process. Next, they explore the forest with adapting environmental factors based on a ’MagicMeter’ indicating the user’s real-time emotional states. The overall goal is to provide more personalized, immersed, and engaging emotional virtual experiences.

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Poster (sa21posters-42 poster.pdf)
MP4 File (3476124.3488657.mp4)
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References

[1]
Vero Vanden Abeele, Katta Spiel, Lennart Nacke, Daniel Johnson, and Kathrin Gerling. 2020. Development and validation of the player experience inventory: A scale to measure player experiences at the level of functional and psychosocial consequences. International Journal of Human-Computer Studies 135 (2020), 102370.
[2]
Guillermo Bernal and Pattie Maes. 2017. Emotional beasts: visually expressing emotions through avatars in VR. In Proceedings of the 2017 CHI conference extended abstracts on human factors in computing systems. 2395–2402.
[3]
Margaret M Bradley and Peter J Lang. 1994. Measuring emotion: the self-assessment manikin and the semantic differential. Journal of behavior therapy and experimental psychiatry 25, 1(1994), 49–59.
[4]
Arindam Dey, Hao Chen, Mark Billinghurst, and Robert W Lindeman. 2018. Effects of Manipulating Physiological Feedback in Immersive Virtual Environments. In Proceedings of the 2018 Annual Symposium on Computer-Human Interaction in Play. ACM, 101–111.
[5]
Kunal Gupta, Jovana Lazarevic, Yun Suen Pai, and Mark Billinghurst. 2020. AffectivelyVR: Towards VR Personalized Emotion Recognition. In 26th ACM Symposium on Virtual Reality Software and Technology. 1–3.
[6]
Javier Marín-Morales, Carmen Llinares, Jaime Guixeres, and Mariano Alcañiz. 2020. Emotion recognition in immersive virtual reality: From statistics to affective computing. Sensors 20, 18 (2020), 5163.
[7]
Morteza Zangeneh Soroush, Keivan Maghooli, Seyed Kamaledin Setarehdan, and Ali Motie Nasrabadi. 2017. A review on EEG signals based emotion recognition. International Clinical Neuroscience Journal 4, 4 (2017), 118.

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Published In

cover image ACM Conferences
SA '21 Posters: SIGGRAPH Asia 2021 Posters
December 2021
87 pages
ISBN:9781450386876
DOI:10.1145/3476124
  • Editors:
  • Shuzo John Shiota,
  • Ayumi Kimura,
  • Wan-Chun Alex Ma
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 December 2021

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Author Tags

  1. EEG
  2. Emotion Adaptive
  3. HRV
  4. Physiological Information
  5. Virtual Reality

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  • Poster
  • Research
  • Refereed limited

Funding Sources

  • Empathic Computing Programme research grant under the Entrepreneurial Universities (EU) initiative of New Zealand

Conference

SA '21
Sponsor:
SA '21: SIGGRAPH Asia 2021
December 14 - 17, 2021
Tokyo, Japan

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Overall Acceptance Rate 178 of 869 submissions, 20%

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