가상현실 기반 3차원 공간에 대한 감정분류 딥러닝 모델
- Title
- 가상현실 기반 3차원 공간에 대한 감정분류 딥러닝 모델
- Other Titles
- Emotion Classification DNN Model for Virtual Reality based 3D Space
- Author
- 전한종
- Keywords
- 가상현실; 감정; 뇌파; 딥러닝; Virtual Reality(VR); Emotion; Electroencephalography(EEG); Fast Fourier Transform(FFT); Deep Learning
- Issue Date
- 2020-04
- Publisher
- 대한건축학회
- Citation
- 대한건축학회논문집 계획계, v. 36, no. 4, page. 41-49
- Abstract
- The purpose of this study was to investigate the use of the Deep Neural Networks(DNN) model to classify user's emotions, in particular Electroencephalography(EEG) toward Virtual-Reality(VR) based 3D design alternatives. Four different types of VR Space were constructed to measure a user's emotion and EEG was measured for each stimulus. In addition to the quantitative evaluation based on EEG data, a questionnaire was conducted to qualitatively check whether there is a difference between VR stimuli. As a result, there is a significant difference between plan types according to the normalized ranking method. Therefore, the value of the subjective questionnaire was used as labeling data and collected EEG data was used for a feature value in the DNN model. Google TensorFlow was used to build and train the model. The accuracy of the developed model was 98.9%, which is higher than in previous studies. This indicates that there is a possibility of VR and Fast Fourier Transform(FFT) processing would affect the accuracy of the model, which means that it is possible to classify a user's emotions toward VR based 3D design alternatives by measuring the EEG with this model.
- URI
- http://koreascience.or.kr/article/JAKO202013461498769.pagehttps://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE09329857https://repository.hanyang.ac.kr/handle/20.500.11754/165354
- ISSN
- 1226-9093; 2384-177X
- DOI
- 10.5659/JAIK_PD.2020.36.4.41
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > ARCHITECTURE(건축학부) > Articles
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