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A Smart-Home IoT Infrastructure for the Support of Independent Living of Older Adults

  • Stefanos Stavrotheodoros
  • Nikolaos Kaklanis
  • Konstantinos Votis
  • Dimitrios Tzovaras
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 520)

Abstract

Although the healthcare sector has been hugely benefited from the advantages made in the Information and Communication Technology (ICT) domain in the recent years, the emerging technology breakthrough of the Internet-of-Things (IoT), in which all devices and services are collaborating while reducing human intervention, promises new solutions that will enable users to have a more home-centric healthcare, and a sustainable active and healthy ageing. This paper is proposing a smart-home IoT infrastructure for the support and extension of the independent living of older adults in their living environments that responds also to real needs of caregivers and public authorities. The proposed infrastructure seamlessly utilizes health and monitoring devices for the provision of a safe environment for an elderly, the mitigation of frailty and the preservation of quality of life and autonomy. It also provides a mechanism for easy setup and testing of the installed equipment and a decision support system that offers advanced data analytics and visual analytics mechanisms to the formal and informal caregivers of the elderly for the efficient monitoring of their health and activity status.

Keywords

Telemedicine Internet of Things Elderly monitoring 

Notes

Acknowledgement

This work is supported by the EU funded project ACTIVAGE (H2020-732679).

References

  1. 1.
    Koop, C.E., Mosher, R., Kun, L., Geiling, J., Grigg, E., Long, S., Macedonia, C., Merrell, R., Satava, R., Rosen, J.: Future delivery of health care: cybercare. IEEE Eng. Med. Biol. Mag. 27(6), 29–38 (2008)CrossRefGoogle Scholar
  2. 2.
    Wiles, J.L.: Home as a new site of health care consumption. In: Andrews, G., Phillips, D.R. (eds.) Aging in Place. Routledge, London (2005)Google Scholar
  3. 3.
    Grabowski, D.: The cost-effectiveness of long-term care services: review and synthesis of the most recent evidence. Med. Care Res. Rev. 63(1), 3–28 (2006)CrossRefGoogle Scholar
  4. 4.
    Sixsmith, A., Mueller, S., Lull, F., Klein, M., Bierhoff, I., Deleaney, S., Byrne, P., Sproll, S., Savage, R., Avatangelou, E.: A user-driven approach to developing ambient assisted living systems for older people: the SOPRANO project. In: Soar, J., Swidell, R., Tsang, P. (eds.) Intelligent Technologies for Bridging the Grey Digital Divide. IGI Global, Hershey (2010)Google Scholar
  5. 5.
    Dohr, A., Modre-Opsrian, R., Drobics, M., Hayn, D., Schreier, G.: The Internet of Things for ambient assisted living. In: Proceedings of the Seventh International Conference on Information Technology: New Generations, pp. 804–809. IEEE Press (2010)Google Scholar
  6. 6.
    Memon, M., Rahr Wagner, S., Pederson Fischer, C., Aysha Beevi, F.H., Overgaard Hansen, F.: Ambient assisted living healthcare frameworks, platforms, standards, and quality attributes. Sensors 14, 4312–4341 (2014)CrossRefGoogle Scholar
  7. 7.
    Gigli, M., Koo, S.: Internet of Things, services and applications categorization. Adv. Internet Things 1, 27–31 (2011)CrossRefGoogle Scholar
  8. 8.
    Stavrotheodoros, S., Kaklanis, N., Tzovaras, D.: A personalized cloud-based platform for AAL support to cognitively impaired elderly people. In: Maglaveras, N., Chouvarda, I., de Carvalho, P. (eds.) Precision Medicine Powered by pHealth and Connected Health. IP, vol. 66, pp. 87–91. Springer, Singapore (2018).  https://doi.org/10.1007/978-981-10-7419-6_15CrossRefGoogle Scholar
  9. 9.
    MyLife project. http://www.mylife-project.org. Accessed 19 Mar 2018
  10. 10.
    Kim, E., Helal, S., Cook, D.: Human activity recognition and pattern discovery. IEEE Pervasive Comput. 9(1), 48–53 (2010)CrossRefGoogle Scholar
  11. 11.
    Van Kasteren, T., Englebienne, G., Krose, B.J.: An activity monitoring system for elderly care using generative and discriminative models. Personal Ubiquit. Comput. 14(6), 489–498 (2010)CrossRefGoogle Scholar
  12. 12.
    Arcelus, A., Jones, M.H., Goubran, R., Knoefel, F.: Integration of smart home technologies in a health monitoring system for the elderly. In: Proceedings of Advanced Information Networking and Applications Workshops 2007, pp. 820–825 (2007)Google Scholar
  13. 13.
    Chen, L., Nugent, C.D., Wang, H.: A knowledge-driven approach to activity recognition in smart homes. IEEE Trans. Knowl. Data Eng. 24(6), 961–974 (2012)CrossRefGoogle Scholar
  14. 14.
    Zhang, Q., Su, Y., Yu, P.: Assisting an elderly with early dementia using wireless sensors data in smarter safer home. In: Liu, K., Gulliver, S.R., Li, W., Yu, C. (eds.) ICISO 2014. IAICT, vol. 426, pp. 398–404. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-642-55355-4_41CrossRefGoogle Scholar
  15. 15.
    Komai, K., Fujimoto, M., Arakawa, Y., Suwa, H., Kashimoto, Y., Yasumoto, K.: Beacon-based multi-person activity monitoring system for day care center. In: 2016 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops), pp. 1–6 (2016)Google Scholar
  16. 16.
    Popleteev, A.: Activity tracking and indoor positioning with a wearable magnet. In: Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers, pp. 253–256 (2015)Google Scholar
  17. 17.
    Belmonte-Fernández, Ó., Puertas-Cabedo, A., Torres-Sospedra, J., Montoliu-Colás, R., Trilles-Oliver, S.: An indoor positioning system based on wearables for ambient-assisted living. Sensors 17(1), 36 (2016)CrossRefGoogle Scholar
  18. 18.
    Santos, A., Macedo, J., Costa, A., Nicolau, M.J.: Internet of Things and smart objects for M-health monitoring and control. Procedia Technol. 16, 1351–1360 (2014)CrossRefGoogle Scholar
  19. 19.
    Yap, J.H., Jeong, D.U.: Design and implementation of ubiquitous ECG monitoring system by using android tablet. In: Han, Y.H., Park, D.S., Jia, W., Yeo, S.S. (eds.) Ubiquitous Information Technologies and Applications. Lecture Notes in Electrical Engineering, vol. 214, pp. 269–277. Springer, Berlin (2013).  https://doi.org/10.1007/978-94-007-5857-5_29CrossRefGoogle Scholar
  20. 20.
    Wang, J., Sun, S., Zhang, K., Zhang, L., Xing, B., Gao, Z.: Smart blood pressure monitoring system based on Internet of Things. In: CHI 2013 (2013)Google Scholar
  21. 21.
    Cao, G., Liu, J.: An IoT application: health care system with android devices. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Torre, C., Taniar, D., Apduhan, B.O., Stankova, E., Wang, S. (eds.) ICCSA 2016. LNCS, vol. 9786, pp. 563–571. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-42085-1_46CrossRefGoogle Scholar
  22. 22.
    Menychtas, A., Tsanakas, P., Maglogiannis, I.: Automated integration of wireless biosignal collection devices for patient-centred decision-making in point-of-care systems. Healthc. Technol. Lett. 3(1), 34–40 (2016)CrossRefGoogle Scholar
  23. 23.
    Menychtas, A., Papadimatos, D., Tsanakas, P., Maglogiannis, I.: On the integration of wearable sensors in IoT enabled mHealth and quantified-self applications. In: Auer, M.E., Tsiatsos, T. (eds.) IMCL 2017. AISC, vol. 725, pp. 77–88. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-75175-7_9CrossRefGoogle Scholar
  24. 24.
    Ali, M., Albasha, L., Al-Nashash, H.: A bluetooth low energy implantable glucose monitoring system. In: 2011 41st European Microwave Conference (EuMC). IEEE, pp. 1265–1268 (2011)Google Scholar
  25. 25.
    Pinto, S., Cabral, J., Gomes, T.: We-care: an IoT-based health care system for elderly people. In: 2017 IEEE International Conference on Industrial Technology (2017)Google Scholar
  26. 26.
    Azimi, I., Rahmani, A.M., Liljeberg, P., Tenhunen, H.: Internet of things for remote elderly monitoring: a study from user-centered perspective. J. Ambient Intell. Humaniz. Comput. 8(2), 273–289 (2017)CrossRefGoogle Scholar
  27. 27.
    Touati, F., Tabish, R.: u-Healthcare system: state-of-the-art review and challenges. J. Med. Syst. 37(3), 1–20 (2013)CrossRefGoogle Scholar
  28. 28.
    Bluetooth Standard. https://www.bluetooth.com/. Accessed 19 Mar 2018
  29. 29.
    ZWave Alliance. http://www.z-wave.com. Accessed 19 Mar 2018
  30. 30.
    ZigBee Alliance. http://www.zigbee.org/. Accessed 19 Mar 2018
  31. 31.
    Small, G.W.: What we need to know about age related memory loss. BMJ 324, 1502–1505 (2002)CrossRefGoogle Scholar
  32. 32.
    Hanke, S., Mayer, C., Hoeftberger, O., Boos, H., Wichert, R., Tazari, M.-R., Wolf, P., Furfari, F.: universAAL - an open and consolidated AAL platform. In: Wichert, R., Eberhard, B. (eds.) Ambient Assisted Living. Advanced Technologies and Societal Change, vol. 63, pp. 127–140. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-18167-2_10CrossRefGoogle Scholar
  33. 33.
    Subash, A.: IoTivity – connecting things in IoT. In: TIZEN Developer Summit, pp. 1–48 (2015)Google Scholar
  34. 34.
    Campo, A.D., Gambi, E., Montanini, L., Perla, D., Raffaeli, L., Spisante, S.: MQTT in AAL systems for home monitoring of people with dementia. In: Proceedings of the 27th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), pp. 1–6 (2016)Google Scholar
  35. 35.
    Asim, M.: A survey on application layer protocols for Internet of Things (IoT). Int. J. Adv. Res. Comput. Sci. 8(3), 996–1000 (2017)MathSciNetGoogle Scholar
  36. 36.
    General Data Protection Regulation (GDPR). https://www.eugdpr.org/. Accessed 19 Mar 2018

Copyright information

© IFIP International Federation for Information Processing 2018

Authors and Affiliations

  • Stefanos Stavrotheodoros
    • 1
  • Nikolaos Kaklanis
    • 1
  • Konstantinos Votis
    • 1
  • Dimitrios Tzovaras
    • 1
  1. 1.Information Technologies InstituteCentre for Research and Technology HellasThessalonikiGreece

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