From Past to Present: Human–Machine Interfaces Evolve Toward Adaptivity

  1. Carrera-Rivera, Angela 2
  2. Larrinaga, Felix 2
  3. Lasa, Ganix 3
  4. Reguera-Bakhache, Daniel 4
  5. Unamuno, Gorka 1
  1. 1 IDEKO, Basque Research and Technology Alliance, Arriaga Kalea, 2, 20870, Elgoibar, Spain
  2. 2 Software and System Engineering and Computer Science Department, Faculty of Engineering, Mondragon Unibertsitatea, Loramendi 4, 20500, Arrasate, Spain
  3. 3 Design Innovation Center (DBZ), Mondragon Unibertsitatea, Loramendi, 4, 20500, Arrasate-Mondragon, Gipuzkoa, Spain
  4. 4 Data Analysis and Cybersecurity Department, Faculty of Engineering, Mondragon Unibertsitatea, Loramendi 4, 20500, Arrasate, Spain
Book:
Future Perspectives on Human-Computer Interaction Research

ISBN: 9783031716966 9783031716973

Year of publication: 2024

Pages: 151-186

Type: Book chapter

DOI: 10.1007/978-3-031-71697-3_7 GOOGLE SCHOLAR lock_openOpen access editor

Abstract

Human–machine interfaces (HMI) facilitate communication between humans and machines, and their importance has increased in modern technology. However, traditional HMIs are often static and do not adapt to individual user preferences or behavior. Adaptive User Interfaces (AUIs) have become increasingly important in providing personalized user experiences. Machine-learning techniques have gained traction in User Experience (UX) research to provide smart adaptations that can reduce user cognitive load. This chapter focuses on the development of adaptive HMIs within industrial contexts, offering a structured framework. It provides an overview of the past and present of HMIs and AUIs, while also outlining prospects for future research. The framework, enhanced by user interactions and Context-Aware Recommendation Systems (CARS), aims to provide tailored adaptations, thereby improving overall UX. A case study highlights real-time remote monitoring in smart factories, improving the ease of use and ease of decision making. The study demonstrates the framework usage in addressing real-world HMI, discussing results, challenges, and limitations.

Funding information

This project is funded by the Basque Government’s Department of Education, Universities, and Research (project Ikerketa Taldeak) and the European Union’s Horizon 2020 program (Marie Sklodowska-Curie Grant No. 814078). Daniel Reguera-Bakhache is part of Mondragon Unibertsitatea’s Intelligent Systems for Industrial Systems research group (IT1676-22), supported by the Basque Government. Angela Carrera-Rivera and Felix Larrinaga are part of the Software Engineering research group (IT1519-22).

Bibliographic References

  • Abrahão S, Insfran E, Sluÿters A, Vanderdonckt J (2021) Model-based intelligent user interface adaptation: challenges and future directions. Softw Syst Model 20(5):1335–1349
  • Acemoglu D, Restrepo P (2020) Robots and jobs: evidence from us labor markets. J Polit Econ 128(6):2188–2244
  • Aghion P, Antonin C, Bunel S, Jaravel X (2023) The effects of automation on labor demand. In: Robots and AI, pp 15–39
  • Alroobaea R, Mayhew PJ (2014) How many participants are really enough for usability studies? In: 2014 Science and Information Conference, pp 48–56. IEEE
  • Aranburu E, Lasa G, Kepa Gerrikagoitia J (2018) Evaluating the human machine interface experience in industrial workplaces. In: Proceedings of the 32nd International BCS Human Computer Interaction Conference, vol 32, pp 1–5
  • Bailly G, Lecolinet E, Nigay L (2016) Visual menu techniques. ACM Comput Surveys (CSUR) 49(4):1–41
  • Belli L, Cirani S, Gorrieri A, Picone M (2015) A novel smart object-driven UI generation approach for mobile devices in the internet of things. In: Proceedings of the 1st International Workshop on Experiences with the Design and Implementation of Smart Objects, pp 1–6
  • Berman A, Thakare K, Howell J, Quek F, Kim J (2021) Howdiy: towards meta-design tools to support anyone to 3d print anywhere. In: 26th International Conference on Intelligent User Interfaces, pp 491–503
  • Bettini C, Brdiczka O, Henricksen K, Indulska J, Nicklas D, Ranganathan A, Riboni D (2010) A survey of context modelling and reasoning techniques. Pervas Mobile Comput 6(2):161–180
  • Brdnik S, Heričko T, Šumak B (2022) Intelligent user interfaces and their evaluation: a systematic mapping study. Sensors 22(15):5830
  • Carrera-Rivera A, Reguera-Bakhache D, Larrinaga F, Lasa G, Garitano I (2023a) Structured dataset of human-machine interactions enabling adaptive user interfaces. Sci Data 10(1):831
  • Carrera-Rivera A, Reguera-Bakhache D, Larrinaga F, Lasa G (2023b) Exploring the transformation of user interactions to adaptive human-machine interfaces. In: Proceedings of the XXIII International Conference on Human Computer Interaction, pp 1–7
  • Carrera-Rivera A, Larrinaga F, Lasa G, Martinez-Arellano G, Unamuno G (2024) AdaptUI: a framework for the development of adaptive user interfaces in smart product-service systems. User Model User Adap Inter. https://doi.org/10.1007/s11257-024-09414-0
  • Champiri ZD, Mujtaba G, Salim SS, Chong CY (2019) User experience and recommender systems. In: 2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), pp 1–5. IEEE
  • Fernandez-Garcia AJ, Iribarne L, Corral A, Wang JZ (2015) Evolving mashup interfaces using a distributed machine learning and model transformation methodology. In: On the Move to Meaningful Internet Systems: OTM 2015 Workshops: Confederated International Workshops: OTM Academy, OTM Industry Case Studies Program, EI2N, FBM, INBAST, ISDE, META4eS, and MSC 2015, Rhodes, Greece, October 26–30, 2015. Proceedings, pp 401–410 (2015). Springer
  • Gan R, Liang J, Ahmad BI, Godsill S (2020) Modeling intent and destination prediction within a Bayesian framework: predictive touch as a usecase. Data-Centric Eng 1:12
  • Gil Y, Garijo D, Khider D, Knoblock CA, Ratnakar V, Osorio M, Vargas H, Pham M, Pujara J, Shbita B et al (2021) Artificial intelligence for modeling complex systems: taming the complexity of expert models to improve decision making. ACM Trans Interact Intell Syst 11(2):1–49
  • Gobert C, Todi K, Bailly G, Oulasvirta A (2019) Sam: a modular framework for self-adapting web menus. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp 481–484
  • Gong C (2009) Human-machine interface: design principles of visual information in human-machine interface design. In: 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics, vol 2, pp 262–265. IEEE
  • Gonçalves TG, Rocha ARC (2019) Development process for intelligent user interfaces: an initial approach. In: Proceedings of the XVIII Brazilian Symposium on Software Quality, pp 210–215
  • Guarino N, Oberle D, Staab S (2009) What is an ontology? In: Handbook on ontologies. Springer, Berlin, pp 1–17
  • Hussain J, Ul Hassan A, Muhammad Bilal HS, Ali R, Afzal M, Hussain S, Bang J, Banos O, Lee S (2018) Model-based adaptive user interface based on context and user experience evaluation. J Multimod User Interf 12:1–16
  • Iqbal MW, Ch NA, Shahzad SK, Naqvi MR, Khan BA, Ali Z (2021) User context ontology for adaptive mobile-phone interfaces. IEEE Access 9:96751–96762
  • Ivergard T, Hunt B (2008) Handbook of control room design and ergonomics: a perspective for the future. CRC Press, Boca Raton
  • Jakob N (2000) Why you only need to test with 5 users. Nielsen Norman Group, Nielsen
  • Jannach D, Zanker M, Felfernig A, Friedrich G (2010) Recommender systems: an introduction. Cambridge University Press, Cambridge
  • Johnston V, Black M, Wallace J, Mulvenna M, Bond R (2019) A framework for the development of a dynamic adaptive intelligent user interface to enhance the user experience. In: Proceedings of the 31st European Conference on Cognitive Ergonomics, pp 32–35
  • Khan I, Khusro S (2020) Towards the design of context-aware adaptive user interfaces to minimize drivers’ distractions. Mobile Inform Syst 2020:1–23
  • Kulkarni S, Rodd SF (2020) Context aware recommendation systems: a review of the state of the art techniques. Comput Sci Rev 37:100255
  • Kumar N, Lee SC (2022) Human-machine interface in smart factory: a systematic literature review. Technol Forecast Soc Chang 174:121284
  • Kumar, N., Prajapati, S.: Challenges for interface designers in designing sensor dashboards in the context of industry 4.0. International Journal of Industrial and Manufacturing Engineering 13(8), 539–542 (2019)
  • Landowska A, Szwoch M, Szwoch W (2016) Methodology of affective intervention design for intelligent systems. Interact Comput 28(6):737–759
  • Li G, Wang L, Ou W (2016) Robust personalized ranking from implicit feedback. Int J Pattern Recognit Artif Intell 30(01):1659001
  • Lindgaard G, Chattratichart J (2007) Usability testing: what have we overlooked? In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp 1415–1424
  • Maleki E, Belkadi F, Boli N, Van Der Zwaag BJ, Alexopoulos K, Koukas S, Marin-Perianu M, Bernard A, Mourtzis D (2018) Ontology-based framework enabling smart product-service systems: application of sensing systems for machine health monitoring. IEEE Internet Things J 5(6):4496–4505
  • Mezhoudi N, Khaddam I, Vanderdonckt J (2015) Toward usable intelligent user interface. In: Human-Computer Interaction: Interaction Technologies: 17th International Conference, HCI International 2015, Los Angeles, CA, USA, August 2–7, 2015, Proceedings, Part II 17. Springer, pp 459–471
  • Miraz MH, Ali M, Excell PS (2021) Adaptive user interfaces and universal usability through plasticity of user interface design. Comput Sci Rev 40:100363
  • Mitchell J, Shneiderman B (1989) Dynamic versus static menus: an exploratory comparison. ACM SigCHI Bull 20(4):33–37
  • Nguyen L et al (2016) A new aware-context collaborative filtering approach by applying multivariate logistic regression model into general user pattern. J Data Anal Inform Process 4(03):124
  • Noyes J, Bransby M (2001) People in control: human factors in control room design, vol 60. IET
  • Ochoa W, Larrinaga F, Pérez A (2023) Context-aware workflow management for smart manufacturing: a literature review of semantic web-based approaches. Future Generation Comput Syst (2023)
  • Oestreich H, Heinz-Jakobs M, Sehr P, Wrede S (2022) Human-centered adaptive assistance systems for the shop floor. In: Human-Technology Interaction: Shaping the Future of Industrial User Interfaces, pp 83–125. Springer, Berlin
  • Orghidan R, Gordan M, Danciu M, Vlaicu A (2013) A prototype for the creation and interactive visualization of 3d human face models. Adv Eng For 8:45–54
  • Papadakis H, Papagrigoriou A, Panagiotakis C, Kosmas E, Fragopoulou P (2022) Collaborative filtering recommender systems taxonomy. Knowl Inf Syst 64(1):35–74
  • Papcun P, Kajáti E, Koziorek J (2018) Human machine interface in concept of industry 4.0. In: 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA), pp 289–296. IEEE
  • Peck EM, Easse E, Marshall N, Stratton W, Perrone LF (2015) Flyloop: a micro framework for rapid development of physiological computing systems. In: Proceedings of the 7th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, pp 152–157
  • Perera C, Zaslavsky A, Christen P, Georgakopoulos D (2013) Context aware computing for the internet of things: a survey. IEEE Commun Surv Tutor 16(1):414–454
  • Pu P, Chen L, Hu R (2011) A user-centric evaluation framework for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp 157–164
  • Smirnov AV, Kashevnik AM, Ponomarev A (2017) Context-based infomobility system for cultural heritage recommendation: tourist assistant—tais. Pers Ubiquit Comput 21:297–311
  • Stefanidi Z, Margetis G, Ntoa S, Papagiannakis G (2022) Real-time adaptation of context-aware intelligent user interfaces, for enhanced situational awareness. IEEE Access 10:23367–23393
  • Stephanidis C (2001) Adaptive techniques for universal access. User Model User Adap Inter 11:159–179
  • Stephanidis C, Paramythis A, Sfyrakis M, Stergiou A, Maou N, Leventis A, Paparoulis G, Karagiannidis C (1998) Adaptable and adaptive user interfaces for disabled users in the Avanti project. In: Intelligence in Services and Networks: Technology for Ubiquitous Telecom Services: 5th International Conference on Intelligence in Services and Networks, IS&N’98 Antwerp, Belgium, May 25–28, 1998 Proceedings 5. Springer, pp 153–166
  • Stumpf S (2019) Horses for courses: making the case for persuasive engagement in smart systems. In: Joint Proceedings of the ACM IUI 2019 Workshops, vol 2327. CEUR
  • Tahir R (2015) Analyzing the intelligence in user interfaces. In: 2015 SAI Intelligent Systems Conference (IntelliSys). IEEE, pp 674–680
  • Tan H-Z, Zhao W, Shen H-H (2018) Adaptive user interface optimization for multi-screen based on machine learning. In: 2018 IEEE 22nd International Conference on Computer Supported Cooperative Work in Design (CSCWD). IEEE, pp 743–748
  • ThoughtWorks (2022) ThoughtWorks Technology Radar, vol 27. https://www.thoughtworks.com/content/dam/thoughtworks/documents/radar/2022/10/trtechnologyradarvol27en.pdf. Accessed 19 Jun 2023
  • Tsandilas T, Schraefel M (2005) An empirical assessment of adaptation techniques. In: CHI’05 Extended Abstracts on Human Factors in Computing Systems, pp 2009–2012
  • Turner, C.J., Ma, R., Chen, J., Oyekan, J.: Human in the loop: Industry 4.0 technologies and scenarios for worker mediation of automated manufacturing. IEEE access 9, 103950–103966 (2021)
  • Völkel ST, Schneegass C, Eiband M, Buschek D (2020) What is “intelligent” in intelligent user interfaces? A meta-analysis of 25 years of IUI. In: Proceedings of the 25th International Conference on Intelligent User Interfaces, pp 477–487
  • Wallace J (2020) A holistic UX methodological framework for measuring the aspects of how dynamic, adaptive and intelligent a software solution is and make recommendations for improvement
  • Yang Q (2018) A novel recommendation system based on semantics and context awareness. Computing 100(8):809–823
  • Zhou J, Sun J, Chen F, Wang Y, Taib R, Khawaji A, Li Z (2015) Measurable decision making with GSR and pupillary analysis for intelligent user interface. ACM Trans Comput Human Interact (ToCHI) 21(6):1–23
  • Zhou X, Peng X, Xie T, Sun J, Ji C, Liu D, Xiang Q, He C (2019) Latent error prediction and fault localization for microservice applications by learning from system trace logs. In: Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, pp 683–694