| id |
caadria2024_43 |
| authors |
Ji, Seung Yeul, Kim, Mi Kyoung and Jun, Han Jong |
| year |
2024 |
| title |
Real-Time User Experience and Emotional State Tracking in Indoor Architectural Spaces Using ChatGPT API and EEG |
| source |
Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 3, pp. 489–498 |
| doi |
https://doi.org/10.52842/conf.caadria.2024.3.489
|
| summary |
Technological advances have revolutionized our perception of human interactions in architectural spaces. In this study, EEG for brainwave analysis, LiDAR for spatial scanning, and ESP32 UWB for position detection were integrated into Unity3D and analyzed using the ChatGPT API. Our goal was to enhance the human experience by visualizing real-time positions, emotions, and reactions in architectural environments. The project started with 3D scanning to create a digital twin model in Unity3D, which was transformed into a virtual space with a 5x5 grid to capture EEG data. The data was analyzed using the Wolfram Mathematica API and a ranking algorithm, complemented by the ChatGPT API, fine-tuned with the SEED dataset for comprehensive emotion recognition. The core feature of the system was heat maps for visualizing emotional responses, using Unity3D's dynamic particle system for a more immersive and three-dimensional representation. This advanced approach provides architects and designers with deeper insight into user-centered space design. In summary, our integrated system demonstrates significant potential for understanding and enhancing the user experience in architectural spaces by providing insight into the impact of design elements on emotional states. It's a step forward in intelligent building and urban design that focuses on human well-being and satisfaction. |
| keywords |
EEG, ChatGPT API, Wolfram Mathematica API, LiDAR Scanners, ESP32 UWB, Unity3D |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (974,376 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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