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.
Similar content being viewed by others
References
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
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
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
Bangor A, Kortum PT, Miller JT (2008) An empirical evaluation of the system usability scale. Intl J Human–Comput Interact 24(6):574–594
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
Borchers JO (2000) Interaction design patterns: Twelve theses. In: Workshop, the hague, vol. 2, p. 3. Citeseer
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
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
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
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
Calvary G, Coutaz J (2014) Introduction to model-based user interfaces. Group Note 7 W3C
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
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
Cleland-Huang J, Mazrouee S, Liguo H, Port D (2007) nfr. https://doi.org/10.5281/zenodo.268542
Cook DJ, Augusto JC, Jakkula VR (2009) Ambient intelligence: Technologies, applications, and opportunities. Pervas Mob Comput 5(4):277–298
Coronato A, Paragliola G (2017) A structured approach for the designing of safe aal applications. Expert Syst Appl 85:1–13
Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quart 319–340
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
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
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
Jones KS (2004) A statistical interpretation of term specificity and its application in retrieval. J Document
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
Minitab L (2019) Minitab. Inc., versã,o 19.1.1
Mohamed MAB, Elmahdy HN (2017) Enhancing the life quality of elderly using ambient intelligent technology (amit). Egypt Comput Sci J 41(3)
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
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
Riehle D, Züllighoven H (1996) Understanding and using patterns in software development. Tapos 2(1):3–13
Salton G, Yang CS (1973) On the specification of term values in automatic indexing. J Document 29(4):351–372
Sauro J, Lewis JR (2016) Quantifying the user experience: Practical statistics for user research. Morgan Kaufmann, Burlington
Schmidt A (2000) Implicit human computer interaction through context. Pers Technol 4(2):191–199. https://doi.org/10.1007/BF01324126
Seffah A (2015) Patterns of HCI design and HCI design of patterns: Bridging HCI design and Model-Driven software engineering. Springer
Seffah A, Taleb M (2012) Tracing the evolution of hci patterns as an interaction design tool. Innov Syst Softw Eng 8(2):93–109
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
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
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
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
Toxboe A (2015) User interface design patterns. http://ui-patterns.com/. Online; Accessed 29 Jan 2020
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
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
Waddell TF, Zhang B, Sundar SS (2015) Human–computer interaction. Int Encycloped Interperson Commun 1–9
Acknowledgements
We thank the National Council for Science and Technology (CONACYT) in Mexico for its support with grant No. 246970.
Author information
Authors and Affiliations
Corresponding author
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
About this article
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 26, 1479–1490 (2022). https://doi.org/10.1007/s00779-020-01505-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00779-020-01505-0