An Ambient Assisted Living System for Telemedicine with Detection of Symptoms

  • A. J. Jara
  • M. A. Zamora-Izquierdo
  • A. F. Gomez-Skarmeta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5602)

Abstract

Elderly people have a high risk of health problems. Hence, we propose an architecture for Ambient Assisted Living (AAL) that supports pre-hospital health emergencies, remote monitoring of patients with chronic conditions and medical collaboration through sharing of health-related information resources (using the European electronic health records CEN/ISO EN13606). Furthermore, it is going to use medical data from vital signs for, on the one hand, the detection of symptoms using a simple rule system (e.g. fever), and on the other hand, the prediction of illness using chronobiology algorithms (e.g. prediction of myocardial infarction eight days before). So this architecture provides a great variety of communication interfaces to get vital signs of patients from a heterogeneous set of sources, as well as it supports the more important technologies for Home Automation. Therefore, we can combine security, comfort and ambient intelligence with a telemedicine solution, thereby, improving the quality of life in elderly people.

Keywords

Telemedicine CEN/ISO EN13606 architecture chronobiology 

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References

  1. 1.
    Walter, A.: Actitudes hacia el envejecimiento de la población en Europa, University of Sheffiel, United Kingdom (1999)Google Scholar
  2. 2.
    United Nations.: World Population Ageing 2007 (2007), http://www.un.org/esa/population/publications/WPA2007/wpp2007.htm
  3. 3.
    Steg, H., et al.: Europe Is Facing a Demographic Challenge - Ambient Assisted Living Offers Solutions. In: VDI/VDE/IT, Berlin, Germany (2006)Google Scholar
  4. 4.
    Wang, S.J., et al.: Using patient-reportable clinical history factors to predict myocardial infarction. Computers in Biology and Medicine 31(1), 1–13 (2001)CrossRefGoogle Scholar
  5. 5.
    Wu, W.H., et al.: MEDIC: Medical embedded device for individualized care. Artificial Intelligence in Medicine 42(2), 137–152 (2008)CrossRefGoogle Scholar
  6. 6.
    Cortes, U., et al.: Intelligent Healthcare Managing: An assistive Technology Approach. In: Sandoval, F., Prieto, A.G., Cabestany, J., Graña, M. (eds.) IWANN 2007. LNCS, vol. 4507, pp. 1045–1051. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Alsinet, T., et al.: Automated monitoring of medical protocols: a secure and distributed architecture. Artificial Intelligence in Medicine 27, 367–392 (2003)CrossRefGoogle Scholar
  8. 8.
    Magrabi, F., et al.: Home telecare: system architecture to support chronic disease management. Engineering in Medicine and Biology Society. In: Proceedings of the 23rd Annual International Conference of the IEEE, vol. 4(25-28), pp. 3559–3562 (2001)Google Scholar
  9. 9.
    Katehakis, D.G., et al.: An architecture for integrated regional health telematics networks. In: Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 3642–3645 (2001)Google Scholar
  10. 10.
    Jih, W.-r., et al.: Context-Aware Service Integration for Elder Care in A Smart Environment. In: AAAI 2006 Workshop, Boston, USA (2006)Google Scholar
  11. 11.
    Li, Y.-C., et al.: Building a generic architecture for medical information exchange among healthcare providers. International Journal of Medical Informatics 61, 2–3 (2001)CrossRefGoogle Scholar
  12. 12.
    Catley, C., et al.: Design of a health care architecture for medical data interoperability and application integration. In: 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society, vol. 3(23-26), pp. 1952–1953 (2002)Google Scholar
  13. 13.
    Maldonado, J.A., et al.: Integration of distributed healthcare information systems: Application of CEN/TC251 ENV13606Google Scholar
  14. 14.
    OpenEHR : CEN Standards, EN13606, a standard for EHR System Communication (2008), http://www.openehr.org/standards/cen.html
  15. 15.
    ISO/IEEE 11073. Point-of-care medical device communication, http://www.iso.org/iso/iso_catalogue/catalogue_tc/catalogue_detail.htm?csnumber=36347
  16. 16.
    Harrison, et al.: Principios de medicina interna. McGraw-Hill, New York (1999)Google Scholar
  17. 17.
    Madrid, J.A., Rol de Lama, M.A.: Cronobiología básica y clínica. Editorial EDITEC RED (2007)Google Scholar
  18. 18.
    Jara, A., Zamora, M.A., Skarmeta, A.: A wearable system for tele-monitoring and tele-assistance of patients with integration of solutions from chronobiology for prediction of illness, AmiForum (2008)Google Scholar
  19. 19.
    Wang, C.-C., et al.: A Rule-Based Disease Diagnostic System Using a Temporal Relationship Model, Fuzzy Systems and Knowledge Discovery. In: Fourth International Conference on FSKD 2007, vol. 4, pp. 109–115 (2007)Google Scholar
  20. 20.
    Palma, J., Juarez, J.M., Campos, M., Marin, R.: Fuzzy theory approach for temporal model-based diagnosis: An application to medical domains. In: Artificial Intelligence in Medicine, October 2006, vol. 38(2), pp. 197–218 (2006)Google Scholar
  21. 21.
    Zamora, M.A., Skarmeta, A.: Sistema Integral de Control, Seguridad y Domotica en edificios Inteligentes. Patent number: P200802506, 08-08-2008. University of Murcia (2008)Google Scholar
  22. 22.

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • A. J. Jara
    • 1
  • M. A. Zamora-Izquierdo
    • 1
  • A. F. Gomez-Skarmeta
    • 1
  1. 1.Faculty of Computer ScienceUniv. Murcia, DIICMurciaSpain

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