id |
caadria2024_516 |
authors |
Guan, Kaitong, Yi, Xinyi, Zhang, Zihuan, Guo, Zhe and Li, Zao |
year |
2024 |
title |
Virtual Space Generation Method Driven by Integrated Multi Sensory Feedback Data |
doi |
https://doi.org/10.52842/conf.caadria.2024.3.529
|
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. 529–538 |
summary |
Ergonomics and human factors have gradually taken center stage in generative design. This study investigated a topic that has not received enough attention: using generative methods to couple visual and auditory aspects in space design. Twenty participants were recruited for two sequential experiments to investigate the workflow of generating virtual space with auditory and visual inputs. First, six genres of music were played to arouse their emotion. To create the fitness function for the genetic algorithm, corresponding EEG data related to meditation and attention was gathered. Subsequently, the genetic algorithm optimized the VR device's spatial structure to obtain a value similar to the initial experiment. Thus, with an algorithm to integrate the EEG data, space and music can be coupled and trigger similar emotional states. The results showed considerable emotional differences between music genres in EEG data and questionnaires. It showed the potential of this workflow to generate stylish space coupling with distinct music. This study innovatively integrates auditory and visual elements, developing an interactive and generative design method with multi-sensory input. It also offers insights into enhancing the immersive experience of wearable VR devices. |
keywords |
Virtual space generative design. Spatial Perception, EEG-intervention, Emotional Modulation, Virtual Reality |
series |
CAADRIA |
email |
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full text |
file.pdf (1,730,414 bytes) |
references |
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|
Ajmal, A., Hollitt, C., Frean, M., & Al-Sahaf, H. (2018)
3D PRINTING ARCHITECTURE'S FUTURE
, 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ), 1-6. https://doi.org/10.1109/IVCNZ.2018.8634752
|
|
|
|
Chen, Y., Huang, A. X., Faber, I., Makransky, G., & Perez-Cueto, F. J. A. (2020)
Assessing the Influence of Visual-Taste Congruency on Perceived Sweetness and Product Liking in Immersive VR
, Foods, 9(4), Article 4. https://doi.org/10.3390/foods9040465
|
|
|
|
Cui, W., Li, Z., Xuan, X., Lu, C., Tang, Q., Zhou, S., & Li, Q. (2022)
Influence of Hospital Outdoor Space on Physiological Electroencephalography (EEG) Feedback of Staff
, HERD: Health Environments Research & Design Journal, 15(1), 239-255. https://doi.org/10.1177/19375867211030701
|
|
|
|
Eerola, T. (2011)
Are the Emotions Expressed in Music Genre-specific? An Audio-based Evaluation of Datasets Spanning Classical, Film, Pop and Mixed Genres
, Journal of New Music Research, 40(4), 349-366. https://doi.org/10.1080/09298215.2011.602195
|
|
|
|
Ergan, S., Shi, Z., & Yu, X. (2018)
Towards quantifying human experience in the built environment: A crowdsourcing based experiment to identify influential architectural design features
, Journal of Building Engineering, 20, 51-59. https://doi.org/10.1016/j.jobe.2018.07.004
|
|
|
|
Hu, M., & Roberts, J. (2020)
Built Environment Evaluation in Virtual Reality Environments-A Cognitive Neuroscience Approach
, Urban Science, 4(4), Article 4. https://doi.org/10.3390/urbansci4040048
|
|
|
|
Huang, W., & Xu, W. (2009)
Interior Color Preference Investigation Using Interactive Genetic Algorithm
, Journal of Asian Architecture and Building Engineering, 8(2), 439-445. https://doi.org/10.3130/jaabe.8.439
|
|
|
|
Katmah, R., Al-Shargie, F., Tariq, U., Babiloni, F., Al-Mughairbi, F., & Al-Nashash, H. (2021)
A Review on Mental Stress Assessment Methods Using EEG Signals
, Sensors (Basel, Switzerland), 21(15), 5043. https://doi.org/10.3390/s21155043
|
|
|
|
Kim, S., Park, H., & Choo, S. (2021)
Effects of Changes to Architectural Elements on Human Relaxation-Arousal Responses: Based on VR and EEG
, International Journal of Environmental Research and Public Health, 18(8), 4305. https://doi.org/10.3390/ijerph18084305
|
|
|
|
Russell, J. A. (1980)
A circumplex model of affect
, Journal of Personality and Social Psychology, 39(6), 1161-1178. https://doi.org/10.1037/h0077714
|
|
|
|
Shabbir Alam, M., Zura A. Jalil, S., & Upreti, K. (2022)
Analyzing recognition of EEG based human attention and emotion using Machine learning
, Materials Today: Proceedings, 56, 3349-3354. https://doi.org/10.1016/j.matpr.2021.10.190
|
|
|
|
Sun, X., & Li, Z. (2021)
Use of electroencephalography (EEG) for comparing study of the external space perception of traditional and modern commercial districts
, Journal of Asian Architecture and Building Engineering, 20(6), 840-857. https://doi.org/10.1080/13467581.2020.1813586
|
|
|
|
Turnbull, D., Barrington, L., Torres, D., & Lanckriet, G. (2008)
Semantic Annotation and Retrieval of Music and Sound Effects
, IEEE Transactions on Audio, Speech, and Language Processing, 16(2), 467-476. https://doi.org/10.1109/TASL.2007.913750
|
|
|
|
Zhang, Z., Li, Z., & Guo, Z. (2022)
EEG-based spatial elements optimisation design method
, Architectural Intelligence, 1(1), 17. https://doi.org/10.1007/s44223-022-00017-6
|
|
|
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last changed |
2024/11/17 22:05 |
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