About the Nature of Kansei Information; from Abstract to Concrete

Carole Bouchard
LCIP, Arts & Måtiers Paris Tech, France

Alexandre Gentner
LCIP, Arts & Måtiers Paris Tech, France

Daniel Esquivel
Toyota Motor Europe, Belgium

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Ingår i: KEER2014. Proceedings of the 5th Kanesi Engineering and Emotion Research; International Conference; Linköping; Sweden; June 11-13

Linköping Electronic Conference Proceedings 100:21, s. 271-285

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Publicerad: 2014-06-11

ISBN: 978-91-7519-276-5

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


Designer’s expertise refers to the scientific fields of emotional design and kansei information. This paper aims to answer to a scientific major issue which is; how to formalize designer’s knowledge; rules; skills into kansei information systems. Kansei can be considered as a psycho-physiologic; perceptive; cognitive and affective process through a particular experience. Kansei oriented methods include various approaches which deal with semantics and emotions; and show the correlation with some design properties. Kansei words may include semantic; sensory; emotional descriptors; and also objects names and product attributes. Kansei levels of information can be seen on an axis going from abstract to concrete dimensions. Sociological value is the most abstract information positioned on this axis. Previous studies demonstrate the values the people aspire to drive their emotional reactions in front of particular semantics. This means that the value dimension should be considered in kansei studies. Through a chain of value-function-product attributes it is possible to enrich design generation and design evaluation processes. This paper describes some knowledge structures and formalisms we established according to this chain; which can be further used for implementing computer aided design tools dedicated to early design. These structures open to new formalisms which enable to integrate design information in a non-hierarchical way. The foreseen algorithmic implementation may be based on the association of ontologies and bag-of-words.


Kansei information; Abstract-Concrete; Formalisms; Early design


1. Bouchard C.; Mantelet F.; Ziakovic D.; Setchi R.; Tang Q.; (2007a). Building a design ontology based on the Conjoint Trends Analysis; I*Prom Virtual Conference; July 2007.

2. Bouchard C.; Mougenot C.; Omhover JFO.; Mantelet F.; Setchi R.; Tang Q.; Aoussat A.; (2007b). Building a domain ontology for designers: towards a Kansei based ontology; I*Prom Virtual Conference; July 2007.

3. Bouchard C.; Omhover JF.; Mougenot C.; Aoussat A.; Westerman SJ.; (2008). TRENDS: A Content-Based Information Retrieval System for Designers; Design Computing and Cognition ’08; 2008; pp 593-611; ISBN 978-1-4020-8727-1.

4. Bouchard; C.; Kim; J.; Aoussat; A.; (2009a). Kansei Information Processing in Design; In proceeding of IASDR 2009.

5. Bouchard C.; Mantelet F.; Aoussat A.; Solves C.; Gonzalez J. C.; Pearce K.; Coleman S. (2009b); A European emotional investigation in the field of shoes design; International Journal of Product Development; Volume 7; Issue 1-2; 2009; pp. 3-27.

6. Bouchard C.; Kim J.; Omhover JF. (2011). Cognitive Designers Activity Study; Formalization; Modelling; and Computation in the Inspirational Phase; in Proc. of the 21st CIRP Design Conference; Daejeon; South Korea; pp. 63-68; ISBN 978-89-89693-29-1.

7. Baek S. & al. (2005a). Matching colours with Kansei vocabulary using similarity measure based on wordnet; ICCSA 2005.

8. Baek S. & al. (2005b). Kansei-based image retrieval associated with color; ICCSA 2005.

9. Banerjee; Pedersen; (2003). Extended gloss overlaps as a measure of semantic relatedness; IJCAI’03 Proceedings of the 18th international joint conference on Artificial intelligence; Pages 805-810.

10. Black J. A.; Kahol K.; Tripathi P.; Kuchi P.; Panchanathan S. (2004). Indexing natural images for retrieval based on kansei factors.

11. Ferecatu M.; Boujemaa N.; Crucianu M. (2008). Semantic interactive image retrieval combining visual and conceptual content description; Multimedia systems; February 2008; Volume 13; Issue 5-6; pp 309-322.

12. Gentner; A.; Bouchard; C.; Favart; C. (2013). Investigating user experience as a composition of components and influencing factors. Proceedings of Int. Association of Societies of Design Research conference.

13. Gentner; A. (2014). Definition and representation of user experience intentions in the early phase of the industrial design process: A focus on the kansei process (Doctoral dissertation). Arts&Métiers ParisTech; Paris; France.

14. Hassenzahl; M. (2010). Experience Design: Technology for All the Right Reasons. San Rafael; CA: Morgan & Claypool Publishers.

15. Hayashi; T.; & Hagiwara; M. (1997). An image retrieval system to estimate impression words from images using a neural network; IEEE International Conference on Systems; Man; and Cybernetics-Computational Cybernetics and Simulation; Vol.1; 150-5; IEEE; New York; NY;1997.

16. Hummels; C.; & Overbeeke; K. (2010). Special issue editorial: Aesthetics of interaction. International Journal of Design; 4 (2); 1-2.

17. Kim J. & al. (2010a). Towards a model of how designers mentally categorize design information; CIRP Journal of Manufacturing Science and Technology; CIRP Journal of Manufacturing Science and Technology 3; pp218-226.

18. Kim J. & al. (2010b). Measuring Semantic and Emotional Responses to Bio-inspired design; Design Creativity 2010; Taura; Toshiharu; Nagai; Yukari (Eds.); 1st Edition.; 2011; XII; 330 p. 191 illus.; Hardcover; ISBN: 978-0-85729-223-0.

19. Kim J. & al. (2013). Emotion finds a way to users from designers: Assessing product images to convey designer’s emotion; Journal of Design Research; 12p; accepted for publication; 4 Sep 2012.

20. Lee; S.H.; Harada; A.; & Stappers; P.J. (2002); Pleasure with Products: Design based on Kansei; published in Green; W. and Jordan; P.; “Pleasure with Products: Beyond usability” ed. Taylor & Francis; London; p. 219-229.

21. Lee; S.H. (2005). Integrating Interactive Product Design Research and Education: The Personality in Interaction Assignment; Crossing Design Boundaries; Taylor and Francis; London.

22. Lévy P.; Yamanaka T.;& Tomico O. (2011). Psychophysiological Applications in Kansei Design; in Ying Dai; Basabi Chakraborty; Minghui Shi (Eds.); Kansei Engineering and Soft Computing: Theory and Practice; IGI Global: Hershey; ISBN 978-1616927974; 2011.

23. Lokman; A.M.; & Nagamachi; M. (2010). Kansei Engineering: A Beginners Persperctive (1st Ed.). UPENA

24. Nagamachi M. (1995). Kansei engineering : a new ergonomic consumer-oriented technology for product development; International Journal of Industrial rgonomics 1 ; p. 3-11

25. Nagamachi; M. (2011). Kansei/Affective Engineering. CRC Press.

26. Nagumo; H. (2000). Color image chart; Chohyung Publishing Co.

27. Naphade; (2006). Semantic features extraction and representation; DELOS MUSCLE Summer School; San Vincenzo; June; 2006.

28. Ocnarescu I.; Labrune J.B.; Bouchard C.; Pain F.; Sciamma D.; & Aoussat A. (2011). An initial framework of aesthetic experience over time; Design and Emotion 2012; London.

29. Ortíz Nicólas; J.C.; & Aurisicchio; M. (2011). A scenario of user experience. In S. J. Culley; B. J. Hicks; T. C. McAloone; T. J. Howard; and P. Badke-Schaub; Proceedings of The 18th International Conference on Engineering Design (ICED 11); Impacting through Engineering Design: Vol. 7. Human Behaviour in Design (pp. 182-193).

30. Overbeeke; C.J.; Djajadiningrat; J.P.; Wensveen; S.A.G.and Hummels; C.C.M. (2000). Neglected aspects of HCI: Fun; beauty and bodily interaction.. In Proceedings of the OZCHI2000; Tutorial

31. Rieuf & al.; (2014). Immersive moodboards; a comparative study of industrial design inspiration material; Submitted in Journal of Design Research; 2014.

32. Rokeach M. (1973). The nature of human values; New York; The free press.

33. Schütte; S.; Eklund; J.; Axelsson; J. R. C.; & Nagamachi; M.; (2004); Concept; Methods and Tools in Kansei ngineering; published in “Theoretical Issues in rgonomics Science”; Vol. (3); p.214-232.

34. Setchi R.; & Bouchard C. (2010). In Search of Design Inspiration: A Semantic-Based Approach; Journal of Computing and Information Science and Engineering; September 2010; Volume 10; Issue 3; 031006 (23 pages); doi:10.1115/1.3482061.

35. Shibata T; & Kato T. (1999). "Kansei image retrieval system for street landscape discrimination and graphical parameters based on correlation of two images"; IEEE-SMC’99 Conference Proceedings-1999 IEEE International.

36. Shieh; M. D.; & Cheng; C. C. (2003). Development of an Intelligent Fabric Retrieval System using Computer -Based Kansei Algorithm”; Journal of the 6th Asian Design International Conference; Vol.1; ISSN 1348-7817; 2003.

37. Singenobu; K. (1990); Color Image Scale; Kodansha America.

38. Tomico O. & al. (2008). Kansei Physiological Measurements And Constructivist Psychological Explorations For Approaching User Subjective Experience; International Design Conference - Design 2008; Dubrovnik - Croatia; May 19 - 22.

39. Valette-Florence P. (1995). Introduction à l’analyse des chaînages cognitifs; Recherche; Application en Marketing; Vol. 9; no. 1; pp. 93-118.

40. Yamanaka; T.; & Lévy; P. (2010). Kansei Science and Kansei Value Creation through Kansei; Behavioral and Brain Sciences; 1-11. In Cosmetic Stage 4 (33).

41. Young; S.; & Feigin; B. (1975). Using the Benefit Chain for Improved Strategy Formulation; Journal of Marketing; Vol. 39; No. 3; pp. 72-74; doi:10.2307/1250907.

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