Abstract
Conventional methods predict emotion directly by measuring equipment like electrode. However, this approach is not suitable for education, especially for children. In this paper, we propose modular Bayesian networks for predicting the emotion with the environment information from the sensors. The Bayesian network is constructed as modules divided by Markov boundary. To evaluate the proposed method, we use data collected from kindergarten classes. The results show more than 84% accuracy and 20 times faster than the single Bayesian network.
| Original language | English |
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| Title of host publication | Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015 |
| Editors | Mario Koppen, Azah Kamilah Muda, Kun Ma, Bing Xue, Hideyuki Takagi, Ajith Abraham |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 298-302 |
| Number of pages | 5 |
| ISBN (Electronic) | 9781467393607 |
| DOIs | |
| Publication status | Published - 2016 Jun 15 |
| Event | 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015 - Fukuoka, Japan Duration: 2015 Nov 13 → 2015 Nov 15 |
Publication series
| Name | Proceedings of the 2015 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015 |
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Other
| Other | 7th International Conference of Soft Computing and Pattern Recognition, SoCPaR 2015 |
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| Country/Territory | Japan |
| City | Fukuoka |
| Period | 15/11/13 → 15/11/15 |
Bibliographical note
Publisher Copyright:© 2015 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition
- Signal Processing
- Control and Optimization
- Modelling and Simulation