User Modeling and User-Adapted Interaction

, Volume 27, Issue 3–5, pp 445–491 | Cite as

A knowledge-based approach to user interface adaptation from preferences and for special needs

  • Claudia Loitsch
  • Gerhard Weber
  • Nikolaos Kaklanis
  • Konstantinos Votis
  • Dimitrios Tzovaras


Moving between devices is omnipresent, but not for people with disabilities or those who require specific accessibility options. Setting up assistive technologies or finding settings to overcome a certain barrier can be a demanding task for people without technical skills. Context-sensitive adaptive user interfaces are advancing, although migrating access features from one device to another is very rarely addressed. In this paper, we describe the knowledge-based component of the Global Public Inclusive Infrastructure that infers how a device shall be best configured at the operating system layer, the application layer and the web layer to meet the requirements of a user including possible special needs or disabilities. In this regard, a mechanism to detect and resolve conflicting accessibility policies as well as recommending preference substitutes is a main requirement, as elaborated in this paper. As the proposed system emulates decision-making of accessibility experts, we validated the automatic deduced configurations against manual configurations of ten accessibility experts. The assessment result shows that the average matching score of the developed system is high. Thus, the proposed system can be considered capable of making precise decisions towards personalizing user interfaces based on user needs and preferences.


Accessibility Adaptive user interfaces Knowledge-based systems Expert systems 


  1. Abascal, J., Aizpurua, A., Cearreta, I., Gamecho, B., Garay, N., Raul, M.: A Modular Approach to User Interface Adaptation for People with Disabilities in Ubiquitous Environments. Technical report, Internal Technical Report No. EHU-KAT-IK-01-11 (2011a)Google Scholar
  2. Abascal, J., Aizpurua, A., Cearreta, I., Gamecho, B., Garay-Vitoria, N., Miñón, R.: Automatically generating tailored accessible user interfaces for ubiquitous services. In: Proceedings of the 13th International ACM SIGACCESS Conference on Computers and Accessibility—ASSETS‘11, pp. 187–194. ACM Press, New York, NY, USA. ISBN 9781450309202 (2011b)Google Scholar
  3. Alharthi, R., Albalawi, R., Abdo, M., El Saddik, A.: A context-aware e-health framework for students with moderate intellectual and learning disabilities. In: Proceedings of the IEEE International Conference on Multimedia and Expo (2011)Google Scholar
  4. Alia, M., Eide, V.S.W., Paspallis, N., Eliassen, F., Hallsteinsen, S.O., Papadopoulos, G.A.: A utility-based adaptivity model for mobile applications. In: Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops—volume 02, pp. 556–563. IEEE (2007)Google Scholar
  5. Altmanninger, K., Wolfram, W.: Accessible graphics in web applications: dynamic generation , analysis and verification. In: Computers Helping People with Special Needs, pp. 378–385 (2008)Google Scholar
  6. Andrich, R.: Towards a global information network: the European Assistive Technology Information Network and the World Alliance of AT Information Providers. In: Gelderblom, G.J., Soede, M., Adriaens, L., Miesenberger, K. (eds.) Everyday Technology for Independence and Care, pp. 190–197. IOS Press, Amsterdam (2011)Google Scholar
  7. Asiry, O., Shen, H., Calder, P.: Extending attention span of ADHD children through an eye tracker directed adaptive user interface. In: Proceedings of the ASWEC 2015 24th Australasian Software Engineering Conference, pp. 149–152. ACM, New York (2015)Google Scholar
  8. Banovic, N., Chevalier, F., Grossman, T., Fitzmaurice, G.: Triggering triggers and burying barriers to customizing software. In: Proceedings of the 2012 ACM Annual Conference on Human Factors in Computing Systems—CHI’12, p. 2717 (2012)Google Scholar
  9. Basman, A.: The Matchmaker Ecology, presented at the Cloud4all Implementers’ Workshop, ICCHP 2012 Conference (2012).
  10. Bautista, S., Hervás, R., Gervás, P., Rojo, J.: Universal Access in the Information Society. An Approach to Treat Numerical Information in the Text Simplification Process (2015)Google Scholar
  11. Bechhofer, S., Harper, S., Lunn, D.: SADIe: semantic annotation for accessibility. In: The Semantic Web-ISWC, pp. 101–115. Springer, Berlin (2006)Google Scholar
  12. Clark, C., Basman, A., Markus, K.G., Zenevich, Y.: Cloud-scale architecture for inclusion: Cloud4all and GPII. In: AAATE, vol. 33, pp. 1366–1371. IOS Press (2013)Google Scholar
  13. Clark, C., Basman, A., Bates, S., Markus, K.G.: Enabling Architecture: How the GPII Supports Inclusive Software Development. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8516 LNCS (PART 4), pp. 368–377 (2014)Google Scholar
  14. Cloud4all: Consolidated evaluation report (D403.1). Technical report, Cloud4all (FP7-289016) (2015).
  15. Coelho, J., Duarte, C.: The Contribution of Multimodal Adaptation Techniques to the GUIDE Interface. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 6765 LNCS, pp. 337–346 (2011)Google Scholar
  16. Coelho, J., Duarte, C., Biswas, P., Langdon, P.: Developing accessible TV applications. In: The proceedings of the 13th International ACM SIGACCESS Conference on Computers and Accessibility, pp. 131–138. ACM (2011)Google Scholar
  17. De Alencar, T.S., Machado, L.R., De Oliveira Neris, De Almeida Neris, Vânia Paula: Addressing the Users’ Diversity in Ubiquitous Environments Through a Low Cost Architecture. Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8515 LNCS (PART 3), pp. 439–450 (2014)Google Scholar
  18. Dias, R., Bermúdez, S.B.I.: AdaptNow—A Revamped Look for the Web: An Online Web Enhancement Tool for the Elderly. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8547 LNCS (PART 1), pp. 113–120 (2014)Google Scholar
  19. Ferretti, S., Mirri, S., Prandi, C., Salomoni, P.: Automatic web content personalization through reinforcement learning. J. Syst. Softw. 0, 1–13 (2016)Google Scholar
  20. Frasincar, F., Houben, G.-J.: Hypermedia presentation adaptation on the semantic web. In: International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, pp. 133–142. Springer, Berlin (2016)Google Scholar
  21. Gajos, K.Z., Weld, D.S., Wobbrock, J.O.: Decision-theoretic user interface generation. In: AAAI”08, pp. 1532–1536 (2008)Google Scholar
  22. Gajos, K.Z., Weld, D.S., Wobbrock, J.O.: Automatically generating personalized user interfaces with supple. Artif. Intell. 174(12), 910–950 (2010)CrossRefGoogle Scholar
  23. Gkatzidou, V., Pearson, E.: A transformation, augmentation, substitution service (TASS) to meet the needs and preferences of the individual learner. In: Proceedings of the 2009 9th IEEE International Conference on Advanced Learning Technologies, ICALT 2009, pp. 98–100 (2009)Google Scholar
  24. Gonzalez-Pisano, J., Rodriguez-Fernandez, M., Gonzalez-Rodriguez, M., Bobes-Bascaran, J., Garcia-Marsa, J.: A system for dynamic adaptation of Web interfaces based on user interaction requirements. In: Computers Helping People with Special Needs, pp. 276–283 (2008)Google Scholar
  25. Good, A., Jerrams-Smith, J.: Enabling accessibility and enhancing web experience: ordering search results according to user needs. In: Universal Access in Human-Computer Interaction, pp. 34–44 (2007)Google Scholar
  26. Google: The New Multi-Screen World Study. Google Think Insights (2012).
  27. Heckmann, D., Schwartz, T., Brandherm, B., Schmitz, M., von Wilamowitz-Moellendorff, M.: Gumo the general user model ontology. In: International Conference on User Modeling, pp. 428-432. Springer, Berlin (2005)Google Scholar
  28. Heckmann, D., Schwarzkopf, E., Mori, J., Dengler, D., Krner, A.: The user model and context ontology gumo revisited for future web 2.0 extensions. In: Contexts and Ontologies: Representation and Reasoning, pp. 37–46 (2007)Google Scholar
  29. Iglesias-Perez, A., Peinado, I., Chacon, J., Ortega-Moral, M.: Frontiers in context modelling to enhance personalisation of assistive technologies. In: Assistive Technology: From Research to Practice—Proceedings of AAATE, pp. 829–834 (2013)Google Scholar
  30. Iglesias-Pérez, A., Loitsch, C., Kaklanis, N., Votis, K., Stiegler, A., Kalogirou, K., Serra-Autonell, G., Tzovaras, D., Weber, G.: Accessibility through Preferences: context-aware recommender of settings. In: Universal Access in Human-Computer Interaction. Design and Development Methods for Universal Access, pp. 224–235. Springer, Berlin (2014)Google Scholar
  31. ISO 9999:2011. Assistive Products for Persons with Disability—Classification and TerminologyGoogle Scholar
  32. ISO/IEC 24751-3:2008(E): Information Technology Individualized Adaptability and Accessibility in e-learning, Education and Training Part 3: Access for All Digital Resource Description (2008)Google Scholar
  33. Kaklanis, N., Biswas, P., Mohamad, Y., Gonzalez, M.F., Peissner, M., Langdon, P., Tzovaras, D.: Towards standardisation of user models for simulation and adaptation purposes. Univers. Access Inf. Soc. 15(1), 21–48 (2016)CrossRefGoogle Scholar
  34. Kaklanis, N., Votis, K., Giannoutakis, K., Tzovaras, D.: A semantic framework for assistive technologies description to strengthen UI adaptation. In: International Conference on Universal Access in Human–Computer Interaction, pp. 236–245. Springer, Berlin (2014)Google Scholar
  35. Kaklanis, N., Votis, K., Giannoutakis, K., Tzovaras, D., Gower, V., Andrich, R.: A unified semantic framework for detailed description of assistive technologies based on the EASTIN taxonomy. In: International Conference on Computers for Handicapped Persons, pp. 275–282. Springer, Berlin (2014)Google Scholar
  36. John, B.E., Kieras, D.E.: The GOMS family of user interface analysis techniques: comparison and contrast. ACM Trans. Comput.–Hum. Interact. 3(4), 320–351 (1996)CrossRefGoogle Scholar
  37. Kane, S.K., Wobbrock, J.O., Harniss, M., Johnson, K.L.: TrueKeys : identifying and correcting typing errors for people with motor impairments. In: Proceedings of the 13th International Conference on Intelligent User Interfaces, pp. 349–352. ACM (2008)Google Scholar
  38. Koester, H., Mankowski, J.: Fully-automatic adjustment for double-click settings. In: Proceedings of the RESNA 2012 Annual Conference. RESNA Press (2012)Google Scholar
  39. Koester, H., Mankowski, J., LoPresti, E., Ashlock, G., Simpson, R.: Software wizards for keyboard and mouse settings: usability for end users. In: Proceedings of the RESNA 2011 Annual Conference. RESNA Press (2011)Google Scholar
  40. Loitsch, C., Stiegler, A., Strobbe, C., Tzovaras, D., Votis, K., Weber, G., Zimmermann, G.: Improving accessibility by matching user needs and preferences. In: AAATE, vol. 33, pp. 1357–1365. IOS Press (2013)Google Scholar
  41. Loitsch, C., Chalkia, E., Bekiaris, E., Weber, G.: Tailored versus prioritized configuration towards accessibility—a study on weighted preferences. In: Universal Access in Human–Computer Interaction. Design and Development Methods for Universal Access, pp. 246–257. Springer, Berlin (2014a)Google Scholar
  42. Loitsch, C., Rütz, P., Grunewald, P., Weber, G.: COMPASS—Eine kollaborative Plattform zur Wissensgenerierung über Accessibility-Probleme und deren Lösungen. In: Gemeinschaften in Neuen Medien (GeNeMe), 2014, pp. 105–116. Technische Universität Dresden (2014b)Google Scholar
  43. Loitsch, C., Hille, D., Weber, G.: Conflict management in multi-user applications for people with disabilities. In: CHI’16 Extended Abstracts, p. 6 (2016)Google Scholar
  44. Mackay, W.E.: Triggers and barriers to customizing software. In: CHI’91 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 153–160. ACM, New York (1991)Google Scholar
  45. Madani, H.H., Ayed, L.J.B., Jemni, M., Sampson, D.G.: Towards accessible and personalized mobile learning for learners with disabilities. In: 2013 4th International Conference on Information and Communication Technology and Accessibility, ICTA 2013 (2013)Google Scholar
  46. Mayer, C., Morandell, M., Gira, M., Sili, M., Petzold, M., Fagel, S., Schüler, C., Bobeth, J., Schmehl, S.: User interfaces for older adults. In: Universal Access in Human–Computer Interaction. User and Context Diversity SE-16, vol. 8010, pp. 142–150 (2013)Google Scholar
  47. Mourouzis, A., Leonidis, A., Foukarakis, M., Antona, M., Maglaveras, N.: A novel design approach for multi-device adaptable user interfaces: concepts, methods and examples. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 6765 LNCS, pp. 400–409 (2011)Google Scholar
  48. Oliveira, R., Abreu, J.F.D., Almeida, A.M.: Promoting IPTV accessibility for visually impaired users: implementation of an adapted service. Procedia Comput. Sci. 27(Dsai 2013), 113–122 (2013)Google Scholar
  49. Paternò, F.: ConcurTaskTrees: An Engineered Approach to Model-based Design of Interactive Systems. The Handbook of Analysis for Human Computer Interaction, pp. 483–503 (2003). ISSN 1467923XGoogle Scholar
  50. Paterno, F.: User Interface Design Adaptation. The Encyclopedia of Human–Computer Interaction (2013).
  51. Peissner, M., Schuller, A., Spath, D.: A design patterns approach to adaptive user interfaces for users with special needs. In: 14th International Conference on Human–Computer Interaction, volume 6761 LNCS, pp. 268–277 (2011)Google Scholar
  52. Peissner, M., Häbe, D., Janssen, D., Sellner, T.: MyUI: Generating accessible user interfaces from multimodal design patterns. In: 4th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, pp. 81–90. ACM, Copenhagen (2012)Google Scholar
  53. Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach, 3rd edn. Prentice Hall, Englewood Cliffs (2009). ISBN 9780136042594MATHGoogle Scholar
  54. Santucci, G.: Vis-A-Wis: improving visual accessibility through automatic Web content adaptation. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 5616 LNCS, pp. 787–796 (2009)Google Scholar
  55. Savidis, A., Stephanidis, C.: Unified user interface design: designing universally accessible interactions. Interact. Comput. 16(2), 243–270 (2004)CrossRefGoogle Scholar
  56. Stephanidis, C., Paramythis, A., Sfyrakis, M., Stergiou, A., Maou, N., Leventis, A., Paparoulis, G., Karagiannidis, C.: Adaptable and adaptive user interfaces for disabled users in the AVANTI project. Architecture 1430, 153 (1998)Google Scholar
  57. Stephanidis, C.: Adaptive techniques for universal access. User Model. User Adapt.Interact. 11(1–2), 159–179 (2001)CrossRefMATHGoogle Scholar
  58. Sunkara, S., Tetali, R., Bose, J.: Responsive, adaptive and user personalized rendering on mobile browsers. In: Proceedings of the 2014 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2014, pp. 259–265 (2014)Google Scholar
  59. Sutterer, M., Droegehorn, O., Klaus, D.: Upos: user profile ontology with situation-dependent preferences support. In: 2008 First International Conference On Advances in Computer–Human Interaction, pp. 230-235. IEEE (2008)Google Scholar
  60. Trewin, S., Zimmermann, G., Vanderheiden, G.: Abstract user interface representations: How well do they support universal access? ACM SIGCAPH Comput. Phys. Handicap. 73–74, 77–84 (2002)CrossRefGoogle Scholar
  61. Tsonos, D., Xydas, G., Kouroupetroglou, G.: Auditory accessibility of metadata in books: a design for all approach. In: UAHCI”07 Proceedings of the 4th International Conference on Universal Access in Human–Computer Interaction: Applications and Services, pp. 436–445 (2007)Google Scholar
  62. Wobbrock, J.O., Fogarty, J., Liu, S.-Y.S., Kimuro, S., Harada, S.: The Angle Mouse: target-agnostic dynamic gain adjustment based on angular deviation. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1401–1410. ACM (2009)Google Scholar
  63. Yang, S.J.H., Shao, N.W.Y.: Enhancing pervasive Web accessibility with rule-based adaptation strategy. Expert Syst. Appl. 32(4), 1154–1167 (2007)CrossRefGoogle Scholar
  64. Zhou, L., Bensal, V., Zhang, D.: Color adaptation for improving mobile web accessibility. In: 2014 IEEE/ACIS 13th International Conference on Computer and Information Science (ICIS), pp. 291—-296. IEEE (2014)Google Scholar
  65. Zouhaier, L., Hlaoui, Y.B., Ayed, L.J.B.: Generating accessible multimodal user interfaces using MDA-based adaptation approach. In: Proceedings-International Computer Software and Applications Conference, pp. 535–540 (2014)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Claudia Loitsch
    • 1
  • Gerhard Weber
    • 1
  • Nikolaos Kaklanis
    • 2
  • Konstantinos Votis
    • 2
  • Dimitrios Tzovaras
    • 2
  1. 1.Department of Computer ScienceTechnische Universität DresdenDresdenGermany
  2. 2.Information Technologies InstituteCentre for Research and Technology HellasThessalonikiGreece

Personalised recommendations