Abstract
The relationship between sensory stimuli and emotion has been actively investigated, but it is relatively undisclosed to determine appropriate stimuli for inducing the target emotion for a group of people in the same space like school, hospital, store, etc. In this paper, we propose a stimuli control system to adjust group emotion in a closed space, especially kindergarten. The proposed system predicts the next emotion of a group of people using modular tree-structured Bayesian networks, and controls the stimuli appropriate to the target emotion using utility table initialized by domain knowledge and adapted by reinforcement learning as the cases of stimuli and emotions are cumulated. To evaluate the proposed system, the real data were collected for five days from a kindergarten where the sensor and stimulus devices were installed. We obtained 84 % of prediction accuracy, and 56.2 % of stimuli control accuracy. Moreover, in the scenario tests on math and music classes, we could control the stimuli to fit the target emotion with 63.2 % and 76.3 % accuracies, respectively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Brave, S., Nass, C.: The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies and Emerging Applications, pp. 81–96. CRC Press, Boca Raton (2003)
Mehrabian, A., Russell, J.: An Approach to Environmental Psychology. MIT Press, Cambridge (1974)
Donovan, R., Rossiter, J.: Store atmosphere: an environmental psychology approach. J. Retail. 58(1), 34–57 (1982)
Black, M., Yacoob, Y.: Tracking and recognizing rigid and non-rigid facial motions using local parametric models of image motion. In: International Conference on Computer Vision, pp. 374–381 (1995)
Eyharabide, V., Amandi, A., Courgeon, M., Clavel, C., Zakaria, C., Martin, J.: An ontology for predicting students’ emotions during a quiz. In: IEEE Workshop on Affective Computational Intelligence, pp. 1–8 (2011)
Yacoub, S., Simske, S., Lin, X., Burns, J.: Recognition of emotions in interactive voice response systems. In: 8th European Conference on Speech Communication and Technology, pp. 1–4 (2003)
Lee, C., Mover, E., Busso, C., Lee, S., Narayanan, S.: Emotion recognition using a hierarchical binary decision tree approach. Speech Commun. 53, 1162–1171 (2011)
Utane, A., Nalbalwar, S.: Emotion recognition through speech using Gaussian mixture model and hidden Markov model. Int. J. Adv. Res. Comput. Sci. Software Eng. 3, 742–746 (2013)
Hayamizu, T., Mutsuo, S., Miyawaki, K., Mori, H., Nishiguchi, S., Yamashita, N.: Group emotion estimation using Bayesian network based on facial expression and prosodic information. In: International Conference on Control System, Computing and Engineering, pp. 23−25 (2012)
Kim, J., Cho, S.-B.: Predicting group emotion in kindergarten classes by modular Bayesian networks. In: 7th International Conference on Soft Computing and Pattern Recognition, pp. 298–302 (2015)
Ivon, A., James, R., Beverly, W.: Using an intelligent tutor and math fluency training to improve math performance. Int. J. Artif. Intell. Educ. 21(1–2), 135–152 (2011)
Yang, K., Cho, S.-B.: Modular dynamic Bayesian network based on Markov boundary for emotion prediction in multi-sensory environment. In: 10th International Conference on Natural Computation, pp. 1131–1136 (2014)
Park, J., Kim, J., Hwang, S.: The study on emotional classification algorithm using Vibraimage technology. In: ACED Asian Conference on Ergonomics and Design (2014)
Romero, C., Ventura, S., Pechenizkiy, M., Baker, RSJd: Handbook of Educational Data Mining, pp. 323–338. CRC Press, Boca Raton (2010)
Acknowledgements
This work was supported by the Industrial Strategic Technology Development Program, 10044828, Development of Augmenting Multisensory Technology for Enhancing Significant Effects on the Service Industry, funded by the Ministry of Trade, Industry and Energy (MI, Korea).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Kim, JH., Kim, KH., Cho, SB. (2016). A Sensory Control System for Adjusting Group Emotion Using Bayesian Networks and Reinforcement Learning. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-32034-2_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32033-5
Online ISBN: 978-3-319-32034-2
eBook Packages: Computer ScienceComputer Science (R0)