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Wireless Sensor Networks for Healthcare on SoA

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System Analysis and Artificial Intelligence

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1107))

Abstract

This article describes how to eliminate the two significant mHealth barriers: the lack of standardization of interoperable services and their absence in general. To minimize these barriers, the service-oriented approach is used—to develop the Repository of services, which can be the service source for any necessary personal healthcare platform for chronic diseases. Monitoring patients’ vital signs parameters (measured at home) is achieved using modern Internet of Things technology and the Body Area Network (BAN). It provides networkable connections between portable diagnostic sensors, patients’ cell phones, cloud data storage with patients’ Personal Health Records, and professional health providers.

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References

  1. Healthcare Analytics Adoption Model: A Framework and Roadmap (white paper). https://www.healthcatalyst.com/white-paper/healthcare-analytics-adoption-model/2/

  2. GREEN PAPER on mobile health (“mHealth” ). http://ec.europa.eu/digital-agenda/en/news/green-paper-mobile-health-mhealth

  3. eHealth Action Plan 2012-2020 - Innovative healthcare for the 21st century. http://ec.europa.eu/health/ehealth/docs/com_2012_736_en.pdf

  4. Discussion paper on semantic and technical interoperability of eHealth. https://health.ec.europa.eu/system/files/2016-11/ev_20121107_wd02_en_0.pdf

  5. eHealth Task Force Report “Redesigning health in Europe for 2020”. http://ec.europa.eu/digital-agenda/en/news/eu-task-force-ehealth-redesigning-health-europe-2020

  6. FHIR - Fast Healthcare Interoperability Resources (FHIR). https://clinicalarchitecture.com/products/fhir/

  7. Yuan, B., Herbert, J.: Web-based real-time remote monitoring for pervasive healthcare. In: Ninth Annual IEEE International Conference on Pervasive Computing and Communications, PerCom 2011, Seattle, WA, USA, Workshop Proceedings. https://doi.org/10.1109/percomw.2011.5766964

  8. Naranjo-Hernández, D., Reina-Tosina, J., Roa, L.M.: Special issue body sensors networks for E-health applications. Sensors (Basel) 20(14), 39–44 (2020). https://doi.org/10.3390/s20143944

  9. Ha, I.: Technologies and research trends in wireless body area networks for healthcare: a systematic literature review. Int. J. Distrib. Sens. Netw. 2015, Article ID 573538, 14. https://doi.org/10.1155/2015/573538

  10. Movassaghi, S., Abolhasan, M., Lipman, J., Smith, D., Jamalipour, A.: Wireless body area networks: a survey. Commun. Surv. Tutor. IEEE 16(3), 1658–1686 (2014)

    Article  Google Scholar 

  11. Landi, H.: What Amazon’s potential move into at-home medical tests could mean for the market, May 19, 2021. https://www.fiercehealthcare.com/tech/what-amazon-s-potential-move-into-at-home-medical-tests-could-mean-for-market

  12. Amazon, Inc. https://www.aws.amazon.com/health

  13. Microsoft: https://www.azure.microsoft.com/en-us/solutions/industries/healthcare/

  14. Alphabet Inc: https://www.cloud.google.com/solutions/healthcare-life-sciences

  15. International Business Machines Corporation: https://www.ibm.com/cloud/healthcare

  16. Lindzon, J.: At-home tests put health in your own hands, April 13, 2021

    Google Scholar 

  17. https://garage.hp.com/us/en/innovation/telemedicine-consumer-healthcare-devices-at-home.html

  18. Competence Centres. https://wiki.egi.eu/wiki/EGI-Engage

  19. Horizon Europe Work Programme 2023-2024: Cluster 1 Health. https://research-and-innovation.ec.europa.eu/funding/fundingopportunities/funding-programmes-and-open-calls/horizon-europe/cluster-1-health_en

  20. Iqbal, O., Iftakhar, T., Ahmad, S.Z.: Internet of things for in home health based monitoring system: modern advances, challenges and future directions. Quest J Softw Eng Simul 7(8), 23–37 (2021). ISSN(Online):2321-3795 ISSN (Print):2321-3809. www.questjournals.org

  21. Omoogun, M., etc.: When eHealth meets the internet of things: pervasive security and privacy challenges. In: 2017 International Conference on Cyber Security and Protection of Digital Services, 19–20 June 2017. London (2017). https://doi.org/10.1109/CyberSecPODS.2017.8074857

  22. Khalid, A., Shahbaz, M.: Using body sensor networks to show that fog computing is more efficient than traditional cloud computing. Int. J. Comput. Sci. Inf. Secur. (IJCSIS), 14(12) (2016). ISSN 1947-5500. https://sites.google.com/site/ijcsis/

  23. Milovanovic, D., Bojkovic, Z.: Cloud-based IoT healthcare applications: requirements and recommendations. Int. J. Internet Things Web Serv. 2, 60–68 (2017). ISSN:2367-9115

    Google Scholar 

  24. Wua, F.-J., Kao, Y.-F., Tseng, Y.-C.: From wireless sensor networks towards cyber physical systems. Pervas. Mobile Comput. 7, 397–413 (2011). https://doi.org/10.1016/j.pmcj.2011.03.003

    Article  Google Scholar 

  25. EGI: Advanced computing for research. https://www.egi.eu/

  26. Flatworld. http://www.flatworldsolutions.com

  27. FI-WARE. http://catalogue.fi-ware.org/enablers

  28. SAP. http://www.sap.com/pc/tech/enterpriseinformation-management

  29. ESRC. http://ukdataservice.ac.uk

  30. Raghupathi, W., Raghupathi, V.: Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2, 3 (2014). https://doi.org/10.1186/2047-2501-2-3

  31. IHTT: Transforming Health Care through Big Data Strategies for leveraging big data in the health care industry. 2013, http://ihealthtran.com/wordpress/2013/03/iht%C2%B2-releases-big-data-research-report-download-today/

  32. LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S., Kruschwitz, N.: Big data, analytics, and the path from insights to value. MIT Sloan Manag. Rev. 52, 20–23 (2011)

    Google Scholar 

  33. Mouttham, A., Peyton, L., El Saddik, A.: Business process integration and management of next-generation health monitoring systems. J. Emerg. Technol. Web Intell. 1(2) (2009)

    Google Scholar 

  34. Petrenko, O.O.: Comparing the types of service architectures. In: System Research and Information Technologies, Kyiv, \(\text{N}^{\underline{\text{ o }}}\)3 (2016). ISSN 1681-6048 (Ukrainian)

    Google Scholar 

  35. Petrenko, A., Bulakh, B.: Automatic service orchestration for e-health application, 2019 in advances in science. Technol. Eng. Syst. J. https://doi.org/10.25046/aj040430

  36. Pysmennyi, I., Kyslyi, R., Petrenko, A.: Edge computing in multi-scope service-oriented mobile healthcare systems. Syst. Res. Inf. Technol. 1, 118–127 (2019). https://dx.doi.org/10.20535/SRIT.2308-8893.2019.1.09

  37. Petrenko, A., Kyslyi, R., Pysmennyi, I.: Detection of human respiration patterns using deep convolution neural networks. Eastern-Eur. J. Enterprise Technol. 4(9), 6–13 (2018)

    Article  Google Scholar 

  38. Petrenko, A., Kyslyi, R., Pysmennyi, I.: Designing security of personal data in distributed health care platform. In: Apr 2018 in technology audit and production reserves. https://doi.org/10.15587/2312-8372.2018.141299

  39. Petrenko, O.O., Petrenko, A.I.: Service-based medical platform for personal health monitoring. Bioinform Proteom Opn Acc J 1(1), 000106 (2017)

    Google Scholar 

  40. Breathmonitor, A.I.: Sleep Apnea Mobile Detector, Studies in Computational Intelligence series: System Analysis and Intelligent Computing: Theory and Applications. Springer Nature, Switzerland AG (2022)

    Google Scholar 

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Petrenko, A., Petrenko, O. (2023). Wireless Sensor Networks for Healthcare on SoA. In: Zgurovsky, M., Pankratova, N. (eds) System Analysis and Artificial Intelligence . Studies in Computational Intelligence, vol 1107. Springer, Cham. https://doi.org/10.1007/978-3-031-37450-0_6

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