Digital Avatars for Older People’s Care

  • Manuel F. BertoaEmail author
  • Nathalie Moreno
  • Alejandro Perez-Vereda
  • David Bandera
  • José M. Álvarez-Palomo
  • Carlos Canal
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1185)


The continuous increase in life expectancy poses a challenge for health systems in modern societies, especially with respect to older people living in rural low-populated areas, both in terms of isolation and difficulty to access and communicate with health services. In this paper, we address these issues by applying the Digital Avatars framework to Gerontechnology. Building on our previous work on mobile and social computing, in particular the People as a Service model, Digital Avatars make intensive use of the capabilities of current smartphones to collect information about their owners, and applies techniques of Complex Event Processing extended with uncertainty for inferring the habits and preferences of the user of the phone and building with them a virtual profile. These virtual profiles allow to monitor the well-being and quality of life of older adults, reminding pharmacological treatments and home health testings, and raising alerts when an anomalous situation is detected.


Gerontechnology Social Computing People as a Service Digital Avatar Complex Event Processing 


  1. 1.
    Graafmans, J.A.M., Brouwers, A.: Gerontechnology, the modeling of normal aging. In: Proceedings of Human Factors Society, 33rd Annual Meeting, Denver, USA (1989)Google Scholar
  2. 2.
    Cruz-Jentoft, A.J., et al.: European silver paper on the future of health promotion and preventive actions, basic research, and clinical aspects of age-related disease. Gerontechnology 7(4), 331–339 (2008)CrossRefGoogle Scholar
  3. 3.
    Triggs, R.: How far we’ve come: a look at smartphone performance over the past 7 year (2015).
  4. 4.
    Guillen, J., Miranda, J., Berrocal, J., Garcia-Alonso, J., Murillo, J.M., Canal, C.: People as a service: a mobile-centric model for providing collective sociological profiles. IEEE Softw. 31(2), 48–53 (2014)CrossRefGoogle Scholar
  5. 5.
    Etzion, O., Niblett, P.: Event Processing in Action. Manning Publications, Stamford (2010)Google Scholar
  6. 6.
    Luckham, D.C.: The Power of Events: An Introduction to Complex Event Processing in Distributed Enterprise Systems. Addison-Wesley, Reading (2002)Google Scholar
  7. 7.
    Miranda, J., et al.: From the internet of things to the internet of people. IEEE Internet Comput. 19(2), 40–47 (2015)CrossRefGoogle Scholar
  8. 8.
    Berrocal, J., Garcia-Alonso, J., Murillo, J., Canal, C.: Rich contextual information for monitoring the elderly in an early stage of cognitive impairment. Pervasive Mobile Comput. 34, 106–125 (2017)CrossRefGoogle Scholar
  9. 9.
    Pérez-Vereda, A., Canal, C.: A people-oriented paradigm for smart cities. In: Cabot, J., De Virgilio, R., Torlone, R. (eds.) ICWE 2017. LNCS, vol. 10360, pp. 584–591. Springer, Cham (2017). Scholar
  10. 10.
    Cugola, G., Margara, A., Pezzè, M., Pradella, M.: Efficient analysis of event processing applications. In: Proceedings of DEBS 2015, pp. 10–21. ACM (2015)Google Scholar
  11. 11.
    Dunkel, J., Bruns, R., Stipković, S.: Event-based smartphone sensor processing for ambient assisted living. In: 2013 IEEE Eleventh International Symposium on Autonomous Decentralized Systems (ISADS), pp. 1–6. IEEE (2013)Google Scholar
  12. 12.
    Bertoa, M.F., Burgueño, L., Moreno, N., Vallecillo, A.: Incorporating measurement uncertainty into OCL/UML primitive datatypes. Softw. Syst. Model. July 2019.
  13. 13.
    Burgueño, L., Bertoa, M.F., Moreno, N., Vallecillo, A.: Expressing confidence in models and in model transformation elements. In: Proceedings of 21th ACM/IEEE International Conference on Model Driven Engineering Languages and Systems - MODELS18, pp. 57–66. ACM (2018)Google Scholar
  14. 14.
    Moreno, N., Bertoa, M.F., Burgueño, L., Vallecillo, A.: Managing measurement and occurrence uncertainty in complex event processing systems. IEEE Access (2019). Scholar
  15. 15.
    Stipkovic, S., Bruns, R., Dunkel, J.: Pervasive computing by mobile complex event processing. In: IEEE 10th International Conference on e-Business Engineering, pp. 318–323. IEEE (2013)Google Scholar
  16. 16.
    Suhothayan, S., Gajasinghe, K., Loku Narangoda, I., Chaturanga, S., Perera, S., Nanayakkara, V.: Siddhi: a second look at complex event processing architectures. In: Proceedings of the 2011 ACM Workshop on Gateway Computing Environments, GCE 2011, pp. 43–50. ACM, New York (2011)Google Scholar
  17. 17.
    Aarts, E., Marzano, S.: The New Everyday: Views on Ambient Intelligence. 010 Publishers, Rotterdam (2003)CrossRefGoogle Scholar
  18. 18.
    Bellavista, P., Corradi, A., Fanelli, M., Foschini, L.: A survey of context data distribution for mobile ubiquitous systems. ACM Comput. Surv. 44(4), 24 (2012)CrossRefGoogle Scholar
  19. 19.
    Raskino, M., Fenn, J., Linden, A.: Extracting value from the massively connected world of 2015. Gartner Research, Technical report G00125949 (2005)Google Scholar
  20. 20.
    Grønli, T.M., Ghinea, G., Younas, M.: Context-aware and automatic configuration of mobile devices in cloud-enabled ubiquitous computing. Pers. Ubiquit. Comput. 18(4), 883–894 (2014)CrossRefGoogle Scholar
  21. 21.
    Makris, P., Skoutas, D.N., Skianis, C.: A survey on context-aware mobile and wireless networking: on networking and computing environments’ integration. IEEE Commun. Surv. Tutor. 15(1), 362–386 (2012)CrossRefGoogle Scholar
  22. 22.
    Park, H.S., Oh, K., Cho, S.B.: Bayesian network-based high-level context recognition for mobile context sharing in cyber-physical system. Int. J. Distrib. Sens. Netw. 7(1), 650387 (2011)CrossRefGoogle Scholar
  23. 23.
    Murphy, M.J., Peterson, M.J.: Sleep disturbances in depression. Sleep Med. Clin. 10(1), 17–23 (2015)CrossRefGoogle Scholar
  24. 24.
    Saeb, S., et al.: Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. J. Med. Int. Res. 17(7), e175 (2015)Google Scholar
  25. 25.
    Agarwal, S., Santra, B., Mukherjee, D.P.: Anubhav: recognizing emotions through facial expression. Vis. Comput. 34(2), 177–191 (2018)CrossRefGoogle Scholar
  26. 26.
    Bonilla, S., Moguel, E., Garcia-Alonso, J.: Facial recognition of emotions with smartphones to improve the elder quality of life. In: García-Alonso, J., Fonseca, C. (eds.) IWoG 2018. CCIS, vol. 1016, pp. 15–25. Springer, Cham (2019). Scholar
  27. 27.
    Dhillon, A., Majumdar, S., St-Hilaire, M., El-Haraki, A.: MCEP: a mobile device based complex event processing system for remote healthcare. In: 2018 IEEE International Conference on Internet of Things (iThings). IEEE, July 2018.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of MalagaMálagaSpain

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