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A Framework for a Fuzzy Smart Home IoT e-Health Support System

  • Moses Adah AganaEmail author
  • Ofem Ajah Ofem
  • Bassey Igbo Ele
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 69)

Abstract

This study provides a conceptual framework for a smart home with an embedded electronic health (e-Health) support, utilizing the Internet of Things (IoT) technology and driven by fuzzy logic. A review of existing frameworks in the related field was made to provide a basis for the proposed framework. The proposed framework was then modeled, and the fuzzy logic system to assist in the decision making process of the system was designed using MATLAB. The fuzzy system was found useful in utilizing inputs from sensors in the smart home to process in the rule base and generate responses to provide support for the sick or aged inhabitants of a smart home. The full scale implementation of the framework in existing smart homes may offer an additional efficiency in the IoT services in healthcare support.

Keywords

Fuzzy e-Health Sensors Actuators Internet Smart 

Notes

Acknowledgements

We acknowledge the foundational works of the IoT Forum in [1] and other pioneering works that guided the design of this study. We equally acknowledge the MathWorks © Group that developed the Fuzzy Logic Toolbox in MATLAB that guided the design of the fuzzy logic component of this study.

References

  1. 1.
    Bauer, M., et al.: Internet of things—architecture: IoT—a deliverable D1.5—Final architectural reference model for the IoT v3.0. Grant agreement no.: 257521. https://iotforum.org/wp-content/uploads/2014/09/D1.5-20130715-VERYFINAL.pdf, pp. 39–488, July 2013
  2. 2.
    Majumder, S., Aghayi, E., Noferesti, M., Memarzadeh-Tehran, H., Mondal, T., Pang, Z., Deen, M.J.: Smart homes for elderly healthcare—recent advances and research challenges. Sens. Rev. www.mdpi.com/journal/sensors. pp. 1–32, Oct 2017
  3. 3.
    Venkat, R.: Global Outlook of the Healthcare Industry. Frost & Sullivan, San Antonio, TX, USA (2015)Google Scholar
  4. 4.
    Canadian Institute for Health Information: National health expenditure trends, 1975 to 2014. Available online: https://www.cihi.ca/en/nhex_2014_report_en.pdf. Accessed on 5 July 2018
  5. 5.
    Hoof, J.V., Demiris, G., Wouters, E.J.M.: Handbook of Smart Homes, Health Care and Well-Being. Springer, Basel, Switzerland (2017)CrossRefGoogle Scholar
  6. 6.
    Anderson, G., Knickman, J.R.: Changing the chronic care system to meet peoples needs. Health Aff. 20, 146–160 (2001)CrossRefGoogle Scholar
  7. 7.
    Du, T.C.-T., Wolfe, P.M.: Implementation of fuzzy logic systems and neural networks in industry. Elsevier: Comput. Ind. 32(3), 261–272 (Mar 1997).  https://doi.org/10.1016/S0166-3615(96)00074-7CrossRefGoogle Scholar
  8. 8.
    Majumder, S., Mondal, T., Deen, M.J.: Wearable sensors for remote health monitoring. Sensors., 17–45 (2017)Google Scholar
  9. 9.
    Pantelopoulos, A., Bourbakis, N.: A survey on wearable sensor-based systems for health monitoring and prognosis. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 40, 1–12 (2010)CrossRefGoogle Scholar
  10. 10.
    Rotariu, C., Pasarica, A., Costin, H., Adochiei, F., Ciobotariu, R.: Telemedicine system for remote blood pressure and heart rate monitoring. In: Proceedings of the 2011 E-Health and Bioengineering Conference (EHB), Iasi, Romania, pp. 1–4, 24–26 Nov 2011Google Scholar
  11. 11.
    Agoulmine, N., Deen, M.J., Leen, J.S., Meyyappan, M.: U-health smart home. IEEE Nanotechnol. Mag. 5, 6–11 (2011)CrossRefGoogle Scholar
  12. 12.
    Mumtaz, S., Alsohaily, A., Pang, Z., Rayes, A., Tsang, K.F., Rodriguez, J.: Massive internet of things for industrial applications: addressing wireless IIoT connectivity challenges and ecosystem fragmentation. IEEE Ind. Electron. Mag. 11, 28–33 (2017)CrossRefGoogle Scholar
  13. 13.
    Meddeb, A.: Internet of things standards: who stands out from the crowd? IEEE Commun. Mag. 54, 40–47 (2016)CrossRefGoogle Scholar
  14. 14.
    Bottazzi, D., Corradi, A., Montanari, R.: Context-aware middleware solutions for anytime and anywhere emergency assistance to elderly people. IEEE Commun. Mag. 44, 82–90 (2006)CrossRefGoogle Scholar
  15. 15.
    Choudhury, B., Choudhury, T.S., Pramanik, A., Arif, W., Mehedi, J.: Design and implementation of an SMS based home security system. In: Proceedings of the 2015 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India, pp. 1–7, 5–7 Mar 2015Google Scholar
  16. 16.
    Bhat, M.I., Ahmad, S., Amin, A., Ashraf, S.: e-Health with internet of things. Int. J. Comput. Sci. Mob. Comput. 6(6), 357–362 (2017)Google Scholar
  17. 17.
    Leister, W., Hamdi, M., Abie, H., Poslad, S., Torjusen, A.: An evaluation framework for adaptive security for the IoT in eHealth. Int. J. Adv. Secur. 7(3 & 4), 93–109 (2014)Google Scholar
  18. 18.
    Du, T.C.-T., Wolfe, P.M.: Implementation of fuzzy logic systems and neural networks in industry. Comput. Ind. 32(3), 261–272 (Mar 1997).  https://doi.org/10.1016/S0166-3615(96)00074-7CrossRefGoogle Scholar
  19. 19.
    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 the 21st International Conference on Advanced Information Networking and Applications Workshops (AINAW’07), Niagara Falls, ON, Canada, vol. 2, pp. 820–825, 21–23 May 2007Google Scholar
  20. 20.
    Abbasi-Kesbi, R., Memarzadeh-Tehran, H., Deen, M.J.: Technique to estimate human reaction time based on visual perception. Health. Technol. Lett. 4, 73–77 (2017)CrossRefGoogle Scholar
  21. 21.
    Amiribesheli, M., Benmansour, A., Bouchachia, A.: A review of smart homes in healthcare. J. Ambient Intell. Humaniz. Comput. 6, 495–517 (2015)CrossRefGoogle Scholar
  22. 22.
    Rantz, M.J., Porter, R.T., Cheshier, D., Otto, D., Servey, C.H., Johnson, R.A., Aud, M., Skubic, M., Tyrer, H., He, Z., et al.: TigerPlace, a state-academic-private project to revolutionize traditional long-term care. J. Hous. Elder. 22, 66–85 (2008)CrossRefGoogle Scholar
  23. 23.
    Anooj, P.K.: Clinical decision support system: risk level prediction of heart disease using weighted fuzzy rules. J. King Saud Univ.—Comput. Inf. Sci. 24(1), 27–40 (2012).  https://doi.org/10.1016/j.jksuci.2011.09.002CrossRefGoogle Scholar
  24. 24.
    Meddeb, A.: Internet of things standards: who stands out from the crowd? IEEE Commun. Mag. 54, 40–47 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Moses Adah Agana
    • 1
    Email author
  • Ofem Ajah Ofem
    • 1
  • Bassey Igbo Ele
    • 1
  1. 1.Department of Computer ScienceUniversity of CalabarCalabarNigeria

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