Kansei Robotics for Safe and Stress-Free Livesphere

Understanding Personal Preferences from Behavior Patterns
  • Takashi SakamotoEmail author
  • Toru Nakata
  • Toshikazu Kato
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9741)


Each person has his own personal feature which includes physical, physiological, psychological and cognitive characteristics. Kansei, i.e., affection, is a mechanism which characterizes each person’s mental and behavioral processes interacting with the world. In this mechanism, multimedia and multimodal information from other people, objects and environment are received physically and physiologically through the five senses related to his age, gender and body conditions. The information is interpreted subjectively to its psychological and cognitive images associated with his personal preference, experiences, knowledge and cultural background. Some decisions are made based on his purpose of actions and his lifestyle. Then, specific behaviors, such as verbal and non-verbal responses, are activated and shown to the other people, objects and environment. “Kansei” is an important perspective for understanding personal preferences and giving suitable assistance to each person. This perspective is supported by the human-centered science and technology of “kansei engineering.” This paper proposes the framework of Kansei modeling through unconscious behavior in interaction in living space to provide safe and stress-free living space.


Kansei engineering Affective engineering Kansei modeling Behavior log and analysis Safe and stress-free livesphere 



The series of these studies are supported by KAKENHI, Japan Society for the Promotion of Science (JSPS), Grant-in-Aid for Scientific Research (S) 19100004, (A) 25240043, and are also supported by Kansei Robotics Research Center, Institute of Science and Engineering, Chuo University.


  1. 1.
    Kato, T.: KANSEI robotics: measurement and modeling of KANSEI from robotics aspect. J. JSAI 21(2), 183–188 (2006). (in Japanese)Google Scholar
  2. 2.
    Kato, T.: Trans-category retrieval based on subjective perception process models. In: Proceedings of IEEE International Conference on Multimedia and EXPO, ICME 2004, June 2004Google Scholar
  3. 3.
    Takeda, Y., Kato, T.: Computational modeling of visual perception and its application to image enhancement. In: Proceedings of KEER 2010 (International Conference on Kansei Engineering and Emotion Research 2010), pp. 1763–1777, March 2010Google Scholar
  4. 4.
    Sea-Ueng, S., Pinyapong, S., Ogino, A., Kato, T.: Prediction of consumer’s intention through their behavior observation in ubiquitous shop space. Kansei Eng. 7(2), 189–195 (2008)CrossRefGoogle Scholar
  5. 5.
    Imamura, N., Suzuki, H., Nagayasu, K., Ogino, A., Kato, T.: Modeling customer preferences for commodities by behavior log analysis with ubiquitous sensing. In: Proceedings of KEER 2010, pp. 1200–1213, March 2010Google Scholar
  6. 6.
    Ogino, A., Imamura, N., Kato, T.: Modeling of human interest in products by observing behaviors of customer in a store. In: Proceedings of KEER 2010, pp. 2072–2081, March 2010Google Scholar
  7. 7.
    Kato, T.: User modeling through unconscious interaction with smart shop. In: Stephanidis, C. (ed.) Universal Access in HCI, Part II, HCII 2011. LNCS, vol. 6766, pp. 61–68. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  8. 8.
    Yomo, A. et al: Estimation of important attributes of items in purchase process for personalized recommendation service. IPSJ SIG Technical reports 2012-HCI-147 (20), pp. 1–8 (2012) (in Japanese)Google Scholar
  9. 9.
    Tajima, T., Iida, Y., Kato, T.: Modeling preferences for commodities by active observation with unforced natural behavior of customers. In: Proceedings of HCI International 2013, pp. 466–474 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.National Institute of Advanced Industrial Science and Technology (AIST)TokyoJapan
  2. 2.Chuo UniversityTokyoJapan

Personalised recommendations