Cooperation Level Estimation of Pair Work Using Top-view Image

  • Katsuya Sakaguchi
  • Kazutaka Shimada
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 272)


To understand an interaction among persons is one of the most important tasks in artificial intelligence. In this paper, we propose a method for estimating a cooperation level in pair work. The task is a cooperation work that take place in front of a whiteboard by two persons. The goal of our study is to provide the cooperation level that is estimated by features extracted from images for teachers. The result of this study is useful for education support systems and problem based learning. We extract the standing location, operation ratio and head direction of each person from an overhead camera. We apply the features to two machine learning approaches: AdaBooost and multiple linear regression. We obtained 77.5% as the accuracy by the AdaBoost and 0.649 as the adjusted R 2 by the regression.


Interaction analysis Cooperation Level Pair Work Top-view Image 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Freund, Y., Schapire, R.E.: Experiments with a new boosting algorithm. In: Proceedings of International Conference on Machine Learning, pp. 148–156 (1996)Google Scholar
  2. 2.
    Grafsgaard, J.F., Boyer, K.E., Wiebe, E.N., Lester, J.C.: Analyzing posture and affect in task-oriented tutoring. In: Proceedings of FLAIRS Conference 2012 (2012)Google Scholar
  3. 3.
    Hmelo-Silver, C.E.: Problem-based learning: What and how do students learn? Educational Psychology Review 16, 235–266 (2004)CrossRefGoogle Scholar
  4. 4.
    Jayagopi, D.B., Sanchez-Cortes, D., Otsuka, K., Yamato, J., Gatica-Perez, D.: Linking speaking and looking behavior patterns with group composition, perception, and performance. In: Proceedings of the International Conference on Multimodal Interaction (ICMI), Santa Monica, USA (2012)Google Scholar
  5. 5.
    Komatsu, K., Shimada, K., Endo, T.: Posture identification for evaluation of multi-party interaction. Technical Report of IEICE, HCS 112, 25–30 (2012)Google Scholar
  6. 6.
    Kouno, D., Shimada, K., Endo, T.: Person identification using top-view image with depth information. In: Proceedings of 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2012), pp. 140–145 (2012)Google Scholar
  7. 7.
    Kumano, S., Otsuka, K., Matsuda, M., Yamato, J.: Understanding empathy/antipathy perceived by external observers based on behavioral coordination and response time. In: Proceedings of HCG Symposium (2012)Google Scholar
  8. 8.
    Mahmoud, M., Baltrušaitis, T., Robinson, P., Riek, L.D.: 3d corpus of spontaneous complex mental states. In: D’Mello, S., Graesser, A., Schuller, B., Martin, J.-C. (eds.) ACII 2011, Part I. LNCS, vol. 6974, pp. 205–214. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Mota, S., Picard, R.W.: Automated posture analysis for detecting learner’s interest level. In: Proceedings of Workshop on Computer Vision and Pattern Recognition for Human-Computer Interaction, CVPR HCI (2003)Google Scholar
  10. 10.
    Nakamura, K., Kakusho, K., Murakami, M., Minoh, M.: Estimating learners’ subjective impressions of the difficulty of course materials in e-learning environments. In: Proceedings of APRU 9th Distance Learning and Internet Canference 2008, pp. 199–206 (2008)Google Scholar
  11. 11.
    Nakatani, R., Kouno, D., Shimada, K., Endo, T.: A person identification method using a top-view head image from an overhead camera. Journal of Advanced Computational Intelligence and Intelligent Informatics 16(5), 696–703 (2012)Google Scholar
  12. 12.
    Omura, M., Shimada, K.: Estimation of subjective impressions of difficulty in quiz dialogue. Technical Report of IEICE NLC2013-2, 7–14 (2013)Google Scholar
  13. 13.
    Quinlan, J.R.: C4.5 Programs for Machine Learning. Morgan Kaufmann Publishers (1993)Google Scholar
  14. 14.
    Shimada, K., Kusumoto, A., Yokoyama, T., Endo, T.: Hot spot detection in multi-party conversation using laughing feature. Technical Report of IEICE NLC2012-7, 25–30 (2012)Google Scholar
  15. 15.
    Takashima, K., Fujita, K., Yokoyama, H., Itoh, Y., Kitamura, Y.: A study of nonverbal cues and subjective atmosphere in six person conversations. In: Proceedings of IEICE SIG-HCS, vol. 112, pp. 49–54 (2012)Google Scholar
  16. 16.
    Vargas, M.F.: Louder than Words: an Introduction to Nonverbal Communication. Iowa State Press (1986)Google Scholar
  17. 17.
    Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann (2011)Google Scholar
  18. 18.
    Yamane, T., Nakamura, K., Ueda, M., Mukunoki, M., Minoh, M.: Detection of interaction between a lecturer and learners based on their actions. In: Proceedings of The Japanese Society for Artificial Intelligence, SIG-ALST(Advanced Learning Science and Technology) (2010)Google Scholar
  19. 19.
    Yokoyama, T., Shimada, K., Endo, T.: Hot spot detection in multi-pary conversation using linguistic and non-linguistic information. In: Proceedings of NLP 2012 (2012) (in Japanese)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Kyushu Institute of TechnologyIizukaJapan

Personalised recommendations