Selection by Visualization of Topological Layouts for Adapted Living Area Design

  • Arnaud Puret
  • Sébastien Aupetit
  • Pierre Gaucher
  • Nicolas Monmarché
  • Mohamed Slimane
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4061)


To design adapted houses many things must be considered: standards and recommendations related to the field of disabilities, capacities and incapacities of the persons, and wishes of the future resident. Considering all those constraints makes difficult to build such adapted living areas. Automatic layout generation simplifies the design task and decreases both design costs and study times. However, it produces a lots of layouts that we need to present to the user. In this work, we proposed clustering and visualization methods to help both the designer and the demander to choose between layouts.


Constraint Satisfaction Problem Assistive Technology Home Automation Node Representative Future Resident 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Leloup, J.: Le projet HM2PH, Habitat Modulaire et Mobile pour Personnes Handicapées, Spécifications d’un espace de vie adapté pour personne en déficit d’autonomie, thèse de doctorat, Université François-Rabelais de Tours, Tours, France (2004)Google Scholar
  2. 2.
    Leloup, J., Gaucher, P., Garcia, J., Siffert, J.: The HMPH Project: Software for the Design of an Adapted Living Area. In: 7th Conference of the Association for the Advancement of Assistive Technology in Europe (AAATE) (2003)Google Scholar
  3. 3.
    Martins Ferreira, J.M., Amaral, T., Santos, D., Agiannidis, A., Edge, M.: The CUSTODIAN Tool: Simple Design of Home Automation Systems for People with Special Needs. In: EIB Scientific Conference, Munique, Allemagne (2000)Google Scholar
  4. 4.
    Leloup, J., Gaucher, P., Pellieux, S., Siffert, J.: The HM2PH project, a database to help the prescription of assistive technologies. In: Miesenberger, K., Klaus, J., Zagler, W., Burger, D. (eds.) ICCHP 2004. LNCS, vol. 3118, Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Gaucher, P.: Chap. 6, Assistance aux actions sur l’environnement, dans Assistance Technique au Handicap, coordination A. Pruski, sous l’égide de l’IFRATH, Hermés (2003)Google Scholar
  6. 6.
    Jo, J.H., Gero, J.S.: Space layout planning using an evolutionary approach. Artificial Intelligence in Engineering Design 11, 245–262 (1998)Google Scholar
  7. 7.
    Kwaiter, G.: Etude et développement d’un modeleur déclaratif 3D temps réel d’environnements virtuels, basés sur les métaheuristiques issues de la recherche locale, thèse de doctorat, Université Paul Sabatier de Toulouse, Toulouse, France (1998)Google Scholar
  8. 8.
    Plemenos, D.: Contribution à l’étude et au développement des techniques de modélisation, génération et visualisation de scènes, thése de doctorat, Université de Nantes, Nantes, France (1991)Google Scholar
  9. 9.
    Medjdoub, B., Yannou, B.: Separating topology and geometry in space planning. Computer-Aided Design 32(1), 39–61 (2000)CrossRefGoogle Scholar
  10. 10.
    Medjdoub, B., Yannou, B.: Dynamic space ordering at a topological level in space planning. Artificial Intelligence in Engineering 15(1), 47–60 (2001)CrossRefGoogle Scholar
  11. 11.
    Card, S.K., Mackinlay, J.D., Shneiderman, B.: Readings in information visualisation: using vision to think. Series in interactive technologies. Morgan Kaufmann Publishers, San Francisco (1999)Google Scholar
  12. 12.
    Borg, I., Groenen, P.: Modern multidimensional scaling: theory and applications. Springer series in statictics. Springer, Heidelberg (1997)MATHGoogle Scholar
  13. 13.
    Aupetit, S.: Contributions aux modéles de Markov cachès: métaheuristiques d’apprentissage, nouveaux modéles et visualisation de dissimilaritè, thése de doctorat, Université François-Rabelais de Tours, Tours, France (2005)Google Scholar
  14. 14.
    MacQueen, J.B.: Some methods for the classification and analysis of multivariate observations. In: Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1, pp. 281–297 (1967)Google Scholar
  15. 15.
    Ward, J.H.: Hierarchical grouping to optimize an objective function. Journal of The American Statistical Association 58, 236–245 (1963)CrossRefMathSciNetGoogle Scholar
  16. 16.
    Wills, G.J.: Nicheworks - interactive visualization of very large graphs. Journal of Computational and Graphical Statistics 8(2), 190–212 (1999)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Arnaud Puret
    • 1
  • Sébastien Aupetit
    • 1
  • Pierre Gaucher
    • 1
  • Nicolas Monmarché
    • 1
  • Mohamed Slimane
    • 1
  1. 1.Laboratoire d’Informatique, Polytech’Tours, Département InformatiqueUniversité François-Rabelais de ToursTOURSFrance

Personalised recommendations