Skip to main content
Log in

Machine learning and dynamic user interfaces in a context aware nurse application environment

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

The increasing usage of smartphones in daily life has received considerable attention in academic and industry driven research to be utilized in the health sector. There has been development of a variety of health-related smartphone applications. Currently, however, there are few to none applications based on nurses’ historical or behavioral preferences. Mobile application development for the health care sector requires extensive attention to security, reliability, and accuracy. In nursing applications, the users are often required to navigate in hospital environments, select patients to support, read the patient history and set action points to assist the patient during their shift. Finally, they have to report their performance on patient related activities and other relevant information before they leave for the day. In a working day, a nurse often visits different locations such as the patient’s room, different laboratories, and offices for filling reports. There is still a limited capability to access context relevant information on a smartphone with minimal recourse such as Wi-Fi triangulation. The Wi-Fi triangulation signals fluctuate significantly for indoor location positioning. Therefore, providing relevant location based services to a mobile subscriber has become challenging. This paper addresses this gap by applying machine learning and behavior analysis to anticipate the potential location of the nurse and provide the required services. The application concept was already presented at the IMCOM 2015 conference. This paper focuses on the process to ascertain a user’s context, the process of analyzing and predicting user behavior, and finally, the process of displaying the information through a dynamically generated UI.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Adomavicius G, Mobasher B, Ricci F, Tuzhilin A (2011) Context-aware recommender systems. AI Mag. 32:67–80

    Google Scholar 

  • Alpaydın E (2014) Introduction to machine learning. Methods Mol Biol 1107:105–128. doi:10.1007/978-1-62703-748-8-7

    Article  MATH  Google Scholar 

  • Beauregard S, Haas H (2006) Pedestrian dead reckoning: a basis for personal positioning. Position Navig Commun 27–35

  • Beyer H, Holtzblatt K (1998) Contextual design: defining customer-centered systems

  • Bishop CMCCM (2006) Pattern recognition and machine learning. Pattern Recognit 4:738. doi:10.1117/1.2819119

    MathSciNet  MATH  Google Scholar 

  • Cannan J, Hu H (2011) Human–machine interaction (HMI): a survey. Gesture 1–16

  • Chen F, Hekler E, Hu J et al (2011) Designing for context-aware health self-monitoring, feedback, and engagement. Proc ACM 2011 Conf Comput Support Coop Work—CSCW’11 613. doi:10.1145/1958824.1958927

  • Coskun V, Ozdenizci B, Ok K (2013) A survey on near field communication (NFC) technology. Wirel Pers Commun 71:2259–2294

    Article  Google Scholar 

  • De Maio C, Fenza G, Gaeta M et al (2011) A knowledge-based framework for emergency DSS. Knowl Based Syst 24:1372–1379. doi:10.1016/j.knosys.2011.06.011

    Article  Google Scholar 

  • Dirin A, Nieminen M (2015) mLUX :usability and user experience development framework for m-learning

  • Dirin M, Dirin A, Laine TH (2015) User-centered design of a context-aware nurse assistant (CANA) at Finnish elderly houses. In: The 9th International Conference on Ubiquitous Information Management and Communication. The Mulia, Bali, Indonesia

  • Domingos P (2012) A few useful things to know about machine learning. Commun ACM 55:78. doi:10.1145/2347736.2347755

    Article  Google Scholar 

  • Eriksen S, Georgsson M, Hofflander M et al (2014) Health in hand: Putting mHealth design in context. In: 2014 IEEE 2nd International Workshop on Usability and Accessibility Focused Requirements Engineering, UsARE 2014—Proceedings, pp 36–39

  • Evennou F, Marx F (2006) Advanced integration of WiFi and inertial navigation systems for indoor mobile positioning. EURASIP J Appl Signal Process. doi:10.1155/ASP/2006/86706

    Google Scholar 

  • Fenza G, Furno D, Loia V (2012) Hybrid approach for context-aware service discovery in healthcare domain. J Comput Syst Sci 78:1232–1247. doi:10.1016/j.jcss.2011.10.011

    Article  MathSciNet  Google Scholar 

  • Ghahramani Z (2004) Unsupervised learning BT—advanced lectures on machine learning. Adv Lect Mach Learn 3176:72–112. doi:10.1007/978-3-540-28650-9_5

    Article  MATH  Google Scholar 

  • James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning. Springer, New York

    Book  MATH  Google Scholar 

  • Kotsiantis SB (2007) Supervised machine learning : a review of classification techniques. Informatica 31:249–268. doi:10.1115/1.1559160

    MathSciNet  MATH  Google Scholar 

  • Lerouge C, Wickramasinghe N, Affiliations A (2013) A review of user-centered design for diabetes-related consumer health informatics technologies. J Diabetes Sci Technol 77:1039–1056. doi:10.1177/193229681300700429

    Article  Google Scholar 

  • Li X, Zhou H, Li L (2013) Tucker tensor regression and neuroimaging analysis. ArXiv 1–28

  • Madlmayr G, Langer J, Kantner C, Scharinger J (2008) NFC devices: security and privacy. In: ARES 2008—3rd International Conference on Availability, Security, and Reliability, Proceedings, pp 642–647

  • Mitchell T (1997) Machine learning. McGraw-Hill, New York

    MATH  Google Scholar 

  • Noble WS (2006) What is a support vector machine? Nat Biotechnol 24:1565–1567. doi:10.1038/nbt1206-1565

    Article  Google Scholar 

  • Norman DA, Draper SW (1986) User centered system design; new perspectives on human–computer interaction. L. Erlbaum Associates Inc., Hillsdale

    Google Scholar 

  • Ojeda L, Borenstein J (2007) Personal dead-reckoning system for GPS-denied environments. In: SSRR2007—IEEE International Workshop on Safety, Security and Rescue Robotics Proceedings

  • Preuveneers D, Berbers Y, Joosen W (2013) The future of mobile e-health application development: exploring HTML5 for context-aware diabetes monitoring. In: Procedia Computer Science, pp 351–359

  • Prgomet M, Georgiou A, Westbrook JI (2009) The impact of mobile handheld technology on hospital physicians’ work practices and patient care: a systematic review. J Am Med Inf Assoc 16:792–801. doi:10.1197/jamia.M3215

    Article  Google Scholar 

  • Ratwani RM, Fairbanks RJ, Hettinger AZ, Benda NC (2015) Electronic health record usability: analysis of the user-centered design processes of eleven electronic health record vendors. J Am Med Inform Assoc 22:1179–1182. doi:10.1093/jamia/ocv050

    Article  Google Scholar 

  • Rendle S, Gantner Z, Freudenthaler C, Schmidt-Thieme L (2011) Fast context-aware recommendations with factorization machines. In: Proc 34th Int ACM SIGIR Conf Res Dev Inf 635–644. doi:10.1145/2009916.2010002

  • Solanas A, Patsakis C, Conti M et al (2014) Smart health: a context-aware health paradigm within smart cities. IEEE Commun Mag 52:74–81. doi:10.1109/MCOM.2014.6871673

    Article  Google Scholar 

  • Su J, Zhang H (2006) A fast decision tree learning algorithm. 21st Natl Conf Artif Intell 1 5:500–505

  • Surendran S, Rasamany S, Megalingam RK (2013) Context aware biomedical robotic platform for elderly health care. In: Proceedings of the 8th International Conference on Computer Science and Education, ICCSE 2013, pp 259–263

  • Tipping ME (2001) Sparse Bayesian learning and the relevance vector machine. J Mach Learn Res 1:211–245. doi:10.1162/15324430152748236

    MathSciNet  MATH  Google Scholar 

  • Van Bogaert P, Meulemans H, Clarke S et al (2009) Hospital nurse practice environment, burnout, job outcomes and quality of care: test of a structural equation model. J Adv Nurs 65:2175–2185. doi:10.1111/j.1365-2648.2009.05082.x

    Article  Google Scholar 

  • Wilkinson CR, De Angeli A (2014) Applying user centred and participatory design approaches to commercial product development. Des Stud 35:614–631. doi:10.1016/j.destud.2014.06.001

    Article  Google Scholar 

  • World Health Organization (2011) mHealth: new horizons for health through mobile technologies. Glob Obs eHealth Ser. ISBN 978 92 4 156425 0

  • Yang C, Shao H (2015) WiFi-based indoor positioning. IEEE Commun Mag 53:150–157. doi:10.1109/MCOM.2015.7060497

    Article  Google Scholar 

  • Zhang H (2004) The optimality of naive Bayes. Proc Seventeenth Int Florida Artif Intell Res Soc Conf FLAIRS 2004(1):1–6. doi:10.1016/j.patrec.2005.12.001

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Teemu H. Laine.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ham, N., Dirin, A. & Laine, T.H. Machine learning and dynamic user interfaces in a context aware nurse application environment. J Ambient Intell Human Comput 8, 259–271 (2017). https://doi.org/10.1007/s12652-016-0384-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-016-0384-1

Keywords

Navigation