Predicting interaction design patterns for designing explicit interactions in ambient intelligence systems: a case study

Abstract

Ambient intelligence (AmI) focuses on supporting people by designing sensitive and responsive environments to context through implicit and explicit interactions. Explicit interactions in AmI systems have requirements specific to making interactions robust, smooth, intuitive, and reliable. Based on requirements, the designers can detect and eliminate faults from the beginning of the design process and understand the users’ needs and demands. This work presents a UIPatternM model for predicting interaction design patterns from processing text-based requirements through machine learning algorithms. We evaluate the predictions of our proposal. We also present a case study with professional designers who evaluated the UIPatternM recommender predictions according to a set of design-level requirements that emulate everyday needs. Our participants performed a set of tasks based on scenarios, and we evaluated the participants’ using effectiveness, efficiency, and satisfaction as performance metrics. Applying the UIPatternM model helped to endorse the conception and refinement of user interface design for explicit interaction in AmI systems.

This is a preview of subscription content, access via your institution.

We’re sorry, something doesn't seem to be working properly.

Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Fig. 1
Fig. 2

References

  1. 1.

    Al-Samarraie H, Ahmad Y (2016) Use of design patterns according to hand dominance in a mobile user interface. J Educ Comput Res 54(6):769–792

    Article  Google Scholar 

  2. 2.

    Bai A, Mork HC, Halbach T, Fuglerud KS, Leister W, Schulz T (2016) A review of universal design in ambient intelligence environments. Smart Accessibility 6–11

  3. 3.

    Bali M, Gore D (2015) A survey on text classification with different types of classification methods. Int J Innov Res Comput Commun Eng 3:4888–4894

    Google Scholar 

  4. 4.

    Bangor A, Kortum PT, Miller JT (2008) An empirical evaluation of the system usability scale. Intl J Human–Comput Interact 24(6):574–594

    Article  Google Scholar 

  5. 5.

    Bastaki BB, Bosakowski T, Benkhelifa E (2017) Intelligent assisted living framework for monitoring elders. In: 2017 IEEE/ACS 14th international conference on computer systems and applications (AICCSA), pp 495–500

  6. 6.

    Borchers JO (2000) Interaction design patterns: Twelve theses. In: Workshop, the hague, vol. 2, p. 3. Citeseer

  7. 7.

    Bradley M, Kristensson PO, Langdon P, Clarkson PJ (2018) Interaction patterns: The key to unlocking digital exclusion assessment?. In: International conference on applied human factors and ergonomics, pp 564–572. Springer

  8. 8.

    Brooke J (1996) SUS-A quick and dirty usability scale. Usability evaluation in industry. CRC Press, Boca Raton. https://www.crcpress.com/product/isbn/9780748404605. ISBN: 9780748404605

    Google Scholar 

  9. 9.

    Byrne C, Collier R, O’Grady M, O’Hare GMP Streitz N, Markopoulos P (eds) (2016) User interface design for ambient assisted living systems. Springer International Publishing, Cham

  10. 10.

    Cabitza F, Fogli D, Lanzilotti R, Piccinno A (2017) Rule-based tools for the configuration of ambient intelligence systems: A comparative user study. Multimed Tools Appl 76(4):5221–5241

    Article  Google Scholar 

  11. 11.

    Calvary G, Coutaz J (2014) Introduction to model-based user interfaces. Group Note 7 W3C

  12. 12.

    Castillo NG, Pérez JL, Gómez-Sanz JJ (2018) A computational approach to improve the gathering of ambient assisted living requirements. In: Multidisciplinary digital publishing institute proceedings, vol. 2, p. 1246

  13. 13.

    Chung ES, Hong JI, Lin J, Prabaker MK, Landay JA, Liu AL (2004) Development and evaluation of emerging design patterns for ubiquitous computing. In: Proceedings of the 5th conference on Designing interactive systems: Processes, practices, methods, and techniques, pp 233–242

  14. 14.

    Cleland-Huang J, Mazrouee S, Liguo H, Port D (2007) nfr. https://doi.org/10.5281/zenodo.268542

  15. 15.

    Cook DJ, Augusto JC, Jakkula VR (2009) Ambient intelligence: Technologies, applications, and opportunities. Pervas Mob Comput 5(4):277–298

    Article  Google Scholar 

  16. 16.

    Coronato A, Paragliola G (2017) A structured approach for the designing of safe aal applications. Expert Syst Appl 85:1–13

    Article  Google Scholar 

  17. 17.

    Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quart 319–340

  18. 18.

    Diamantopoulos T, Roth M, Symeonidis A, Klein E (2017) Software requirements as an application domain for natural language processing. Lang Resour Eval 51(2):495–524

    Article  Google Scholar 

  19. 19.

    Engel J, Märtin C., Forbrig P (2015) A concerted model-driven and pattern-based framework for developing user interfaces of interactive ubiquitous applications. In: LMIS@ EICS, pp 35–41

  20. 20.

    Gallardo J, Bravo C, Molina AI (2018) A framework for the descriptive specification of awareness support in multimodal user interfaces for collaborative activities. J Multimodal User Interf 12(2):145–159. https://doi.org/10.1007/s12193-017-0255-x

    Article  Google Scholar 

  21. 21.

    Jones KS (2004) A statistical interpretation of term specificity and its application in retrieval. J Document

  22. 22.

    Mandl T, Kirisci PT, Thoben KD (2018) A method for designing physical user interfaces for intelligent production environments. Adv Human-Comput Interact 2018:6487070. https://doi.org/10.1155/2018/6487070

    Article  Google Scholar 

  23. 23.

    Minitab L (2019) Minitab. Inc., versã,o 19.1.1

  24. 24.

    Mohamed MAB, Elmahdy HN (2017) Enhancing the life quality of elderly using ambient intelligent technology (amit). Egypt Comput Sci J 41(3)

  25. 25.

    Pranckevičius T, Marcinkevičius V (2017) Comparison of naive bayes, random forest, decision tree, support vector machines, and logistic regression classifiers for text reviews classification. Baltic J Modern Comput 5(2):221

    Article  Google Scholar 

  26. 26.

    Ramos C (2009) An architecture for ambient intelligent environments. In: Corchado JM, Tapia DI, Bravo J (eds) 3rd symposium of ubiquitous computing and ambient intelligence 2008. Springer, Berlin, pp 30–38

  27. 27.

    Riehle D, Züllighoven H (1996) Understanding and using patterns in software development. Tapos 2(1):3–13

    Google Scholar 

  28. 28.

    Salton G, Yang CS (1973) On the specification of term values in automatic indexing. J Document 29(4):351–372

    Article  Google Scholar 

  29. 29.

    Sauro J, Lewis JR (2016) Quantifying the user experience: Practical statistics for user research. Morgan Kaufmann, Burlington

    Google Scholar 

  30. 30.

    Schmidt A (2000) Implicit human computer interaction through context. Pers Technol 4(2):191–199. https://doi.org/10.1007/BF01324126

    Article  Google Scholar 

  31. 31.

    Seffah A (2015) Patterns of HCI design and HCI design of patterns: Bridging HCI design and Model-Driven software engineering. Springer

  32. 32.

    Seffah A, Taleb M (2012) Tracing the evolution of hci patterns as an interaction design tool. Innov Syst Softw Eng 8(2):93–109

    Article  Google Scholar 

  33. 33.

    Serral E, Sernani P, Dragoni AF, Dalpiaz F (2017) Contextual requirements prioritization and its application to smart homes. In: European conference on ambient intelligence, pp 94–109. Springer

  34. 34.

    Silva-Rodríguez V, Nava-Muñoz SE, Martínez-Pérez FE, Pérez-González HG (2018) How to select the appropriate pattern of human-computer interaction?: A case study with junior programmers. In: 2018 6Th international conference in software engineering research and innovation (CONISOFT), pp 66–71. IEEE

  35. 35.

    Streitz N, Charitos D, Kaptein M, Böhlen M. (2019) Grand challenges for ambient intelligence and implications for design contexts and smart societies. J Ambient Intell Smart Environ 11(1):87–107

    Article  Google Scholar 

  36. 36.

    Thanh-Diane N, Vanderdonckt J, Seffah A (2016) Uiplml: Pattern-based engineering of user interfaces of multi-platform systems. In: Research challenges in information science (RCIS), 2016 IEEE tenth international conference on, pp 1–12. IEEE

  37. 37.

    Toxboe A (2015) User interface design patterns. http://ui-patterns.com/. Online; Accessed 29 Jan 2020

  38. 38.

    Vanderdonckt J, Simarro FM (2010) Generative pattern-based design of user interfaces. In: Proceedings of the 1st international workshop on pattern-driven engineering of interactive computing systems, pp 12–19. ACM

  39. 39.

    Vega-Barbas M, Pau I, Augusto JC, Seoane F (2017) Interaction patterns for smart spaces: A confident interaction design solution for pervasive sensitive iot services. IEEE Access 6:1126–1136

    Article  Google Scholar 

  40. 40.

    Waddell TF, Zhang B, Sundar SS (2015) Human–computer interaction. Int Encycloped Interperson Commun 1–9

Download references

Acknowledgements

We thank the National Council for Science and Technology (CONACYT) in Mexico for its support with grant No. 246970.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Viridiana Silva-Rodríguez.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Silva-Rodríguez, V., Nava-Muñoz, S.E., Castro, L.A. et al. Predicting interaction design patterns for designing explicit interactions in ambient intelligence systems: a case study. Pers Ubiquit Comput (2021). https://doi.org/10.1007/s00779-020-01505-0

Download citation

Keywords

  • Interaction design patterns
  • Ambient intelligence systems
  • Design-level requirements
  • Natural language processing
  • Explicit interaction