IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i17p6716-d400991.html
   My bibliography  Save this article

Deep-Learning-Based Stress-Ratio Prediction Model Using Virtual Reality with Electroencephalography Data

Author

Listed:
  • Seung Yeul Ji

    (School of Architecture, Hanyang University, Seoul 04763, Korea)

  • Se Yeon Kang

    (School of Architecture, Hanyang University, Seoul 04763, Korea)

  • Han Jong Jun

    (School of Architecture, Hanyang University, Seoul 04763, Korea)

Abstract

The Reich Chancellery, built by Albert Speer, was designed with an overwhelming ambience to represent the worldview of Hitler. The interior of the Reich Chancellery comprised high-ceiling and low-ceiling spaces. In this study, the change in a person’s emotions according to the ceiling height while moving was examined through brain wave experiments to understand the stress index for each building space. The Reich Chancellery was recreated through VR, and brain wave data collected per space were processed through a first and second analysis. In the first analysis, beta wave changes related to the stress index were calculated, and the space with the highest fluctuation was analyzed. In the second analysis, the correlation between 10 different types of brain waves and waveforms was analyzed; deep-learning algorithms were used to verify the accuracy and analyze spaces with a high stress index. Subsequently, a deep-learning platform for calculating such a value was developed. The results showed that the change in stress index scores was the highest when entering from the Mosaic Hall (15 m floor height) to the Führerbunker (3 m floor height), which had the largest floor height difference. Accordingly, a stress-ratio prediction model for selecting a space with a high stress level was established by monitoring the architectural space based on brain wave information in a VR space. In the architectural design process, the ratio can be used to reflect user sensibility in the design and improve the efficiency of the design process.

Suggested Citation

  • Seung Yeul Ji & Se Yeon Kang & Han Jong Jun, 2020. "Deep-Learning-Based Stress-Ratio Prediction Model Using Virtual Reality with Electroencephalography Data," Sustainability, MDPI, vol. 12(17), pages 1-19, August.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:6716-:d:400991
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/17/6716/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/12/17/6716/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:17:p:6716-:d:400991. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.