Complex Event Processing for Health Monitoring

  • Alejandro Pérez-VeredaEmail author
  • Daniel Flores-Martín
  • Carlos Canal
  • Juan M. Murillo
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1016)


The increase of the life expectancy has become a challenge in regions with a low population density. This fact is caused by the existence of small towns all far from one another and with the peculiarity of many elders with special health care living there. This situation increases in a high percentage the health costs of the region having to attend daily all these elders who need a close monitoring. We live in a IoT era with a huge quantity of new connected devices with lots of sensors. Taking advantage of this, it is possible to monitor these elders from the distance without having to cover the complete area of the region every day. This way, our approach is using a mobile centric architecture that permits the elders having a device which infers a health virtual profile of them with data from its sensors and from other smart devices like bands with pulsometers. At this point we propose using Complex Event Processing techniques to combine the data coming from all sources and analyze it to extract meaningful information for the doctors and caregivers and even detect important events like falls in real time.


Internet of Things Internet of People People as a Service Virtual user profiles Complex Event Processing Elders Gerontechnology 



This work has been partially financed by the Spanish Government through projects TIN2015-67083-R and TIN2015-69957-R (MINECO/FEDER, UE), by the 4IE project 0045-4IE-4-P funded by the Interreg V-A España-Portugal (POCTEP) 2014–2020 program, and by the Regional Government of Extremadura (project GR15098).


  1. 1.
    Perera, C., Liu, C.H., Jayawardena, S., Chen, M.: Context-aware computing in the internet of things: a survey on internet of things from industrial market perspective. CoRR (2015)Google Scholar
  2. 2.
    Do, T.M., Loke, S.W., Liu, F.: Healthylife: an activity recognition system with smartphone using logic-based stream reasoning. In: Mobile and Ubiquitous Systems: Computing, Networking, and Services, pp. 188–199. Springer, Heidelberg (2013)Google Scholar
  3. 3.
    de Prado, A.G., Ortiz, G., Boubeta-Puig, J.: CARED-SOA: a context-aware event-driven service-oriented architecture. IEEE Access 5, 4646–4663 (2017)CrossRefGoogle Scholar
  4. 4.
    Kassianos, A., Emery, J., Murchie, P., Walter, F.: Smartphone applications for melanoma detection by community, patient and generalist clinician users: a review. Br. J. Dermatol. 172(6), 1507–1518 (2015)CrossRefGoogle Scholar
  5. 5.
    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), 48–59 (2014)CrossRefGoogle Scholar
  6. 6.
    Luckham, D.: Event Processing for Business: Organizing the Real-Time Enterprise. Wiley, Hoboken (2011)Google Scholar
  7. 7.
    Marzano, S.: The New Everyday: Views on Ambient Intelligence. 010 Publishers, Rotterdam (2003)Google Scholar
  8. 8.
    Dunkel, J., Bruns, R., Stipkovic, S.: Event-based smartphone sensor processing for ambient assisted living. In: IEEE Eleventh International Symposium on Autonomous Decentralized Systems (ISADS), pp. 1–6 (2013)Google Scholar
  9. 9.
    Bellavista, P., Corradi, A., Fanelli, M., Foschini, L.: A survey of context data distribution for mobile ubiquitous systems. ACM Comput. Surv. 44(4), 1–45 (2012)CrossRefGoogle Scholar
  10. 10.
    Raskino, M., Fenn, J., Linden, A.: Extracting value from the massively connected world of 2015 (2015)Google Scholar
  11. 11.
    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. Networks 7(1) (2011). Scholar
  12. 12.
    Gronli, 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
  13. 13.
    Makris, P., Skoutas, D.N., Skianis, C.: A survey on context-aware mobile and wirelessnet-working: on networking and computing environments’ integration. IEEE Commun. Surv. Tutorials 15(1), 362–386 (2013)CrossRefGoogle Scholar
  14. 14.
    Taub, D., Lupton, E., Hinman, R., Leeb, S., Zeisel, J., Blackler, S.: The escort system: a safety monitor for people living with alzheimer’s disease. IEEE Pervasive Comput. 10(2), 68–77 (2011)CrossRefGoogle Scholar
  15. 15.
    Saeb, S., et al.: Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study. J. Med. Internet Res. 17(7), e175 (2015)CrossRefGoogle Scholar
  16. 16.
    Muhlfeit, J., Melina, C.: The Positive Leader. Pearson Education Limited, Kustantaja (2012)Google Scholar
  17. 17.
    Murphy, M.J., Peterson, M.J.: Sleep disturbances in depression. Sleep Med. Clin. 10(1), 17–23 (2015)CrossRefGoogle Scholar
  18. 18.
    EsperTech – Esper (2017).
  19. 19.
    Eggum, M.: Asper - Esper for Android (2014).

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Alejandro Pérez-Vereda
    • 1
    Email author
  • Daniel Flores-Martín
    • 2
  • Carlos Canal
    • 1
  • Juan M. Murillo
    • 2
  1. 1.University of MalagaMalagaSpain
  2. 2.University of ExtremaduraCáceresSpain

Personalised recommendations