Advertisement

Big-Data Based Real-Time Interactive Growth Management System in Wireless Communications

  • Jonghun Kim
  • Heetae Jang
  • Jong Tak Kim
  • Hee-Jun Pan
  • Roy C. Park
Article
  • 14 Downloads

Abstract

Obesity in children and adolescents has become a severe social issue worldwide. More than 85% of obesity in children and adolescents develops into adult obesity or leads to adult diseases like high blood pressure, artery hardening, and diabetes because of unbalanced growth and development. For this reason, a long-term and systematic care system needs to be developed using wireless communications technologies. Although many of the world’s governments have tried a variety of obesity-care policies, a poor care system remains in the children- and adolescent-healthcare areas. Therefore, this study proposes the big-data-based real-time interactive growth management (RIGM) system for the integrated growth and development of children and adolescents in the wireless communications environment. In the development of the RIGM, the activity, heart rate, steps, and other kinds of bio-data that can be received from a smart device are monitored; the growth and development status is analyzed comprehensively in the platform that receives the bio-data through wireless communications, and it is interactively checked by an application in real time. After the designed child and adolescent growth-management system was tested, the possibility of its use as a systematic growth-management system was confirmed.

Keywords

Wireless communication Big-data Platform Child and youth Data mining 

Notes

Acknowledgements

This research was supported by Incheon Business Information Technopark.

References

  1. 1.
    Güngör, N. K. (2014). Overweight and obesity in children and adolescents. Journal of Clinical Research in Pediatric Endocrinology, 6(3), 129–143.CrossRefGoogle Scholar
  2. 2.
    Jung, H., & Chung, K. (2016). Knowledge-based dietary nutrition recommendation for obese management. Information Technology and Management, 17(1), 29–42.CrossRefGoogle Scholar
  3. 3.
    Musaiger, A. O., Al-Mannai, M., & Al-Marzog, Q. (2014). Overweight and obesity among children (10–13 years) in Bahrain: A comparison between Two International Standards. Pakistan Journal of Medical Sciences, 30(3), 497–500.Google Scholar
  4. 4.
    Nader, P. R., O’Brien, M., Houts, R., Bradley, R., Belsky, J., Crosnoe, R., et al. (2006). Identifying risk for obesity in early childhood. Pediatrics, 118(3), 594–601.CrossRefGoogle Scholar
  5. 5.
    Stark, M. J., Niederhauser, V. P., Camacho, J. M., & Shirai, L. (2011). The prevalence of overweight and obesity in children at a health maintenance organization in Hawai’i. Hawai’i Medical Journal, 70(7), 27–31.Google Scholar
  6. 6.
    Sothern, M. S. (2004). Obesity prevention in children: physical activity and nutrition. Nutrition, 20(7–8), 704–708.CrossRefGoogle Scholar
  7. 7.
    McPherson, A. C., Keith, R., & Swift, J. A. (2014). Obesity prevention for children with physical disabilities: a scoping review of physical activity and nutrition interventions. Journal of Disability and Rehabilitation, 36(19), 1573–1587.CrossRefGoogle Scholar
  8. 8.
    Christodoulos, A. D., Flouris, A. D., & Tokmakidis, S. P. (2006). Obesity and physical fitness of pre-adolescent children during the academic year and the summer period: effects of organized physical activity. Journal of Child Health Care, 10(3), 199–212.CrossRefGoogle Scholar
  9. 9.
    Krebs, N. F., Jacobson, M. S., & American Academy of Pediatrics Committee on Nutrition. (2003). Prevention of pediatric overweight and obesity. Pediatrics, 112(2), 424–430.CrossRefGoogle Scholar
  10. 10.
    Amin, R. U., & Inayat, I. (2017). An empirical study on acceptance of secure healthcare service in Malaysia, Pakistan, and Saudi Arabia: a mobile cloud computing perspective. Annals of Wireless Communications, 72(5–6), 253–264.Google Scholar
  11. 11.
    Cho, E. Y., Kim, J. H., Chung, K. Y., & Park, D. K. (2014). Mobile healthcare application with EMR interoperability for diabetes patients. Cluster Computing, 17(3), 871–880.CrossRefGoogle Scholar
  12. 12.
    Lee, J. Y., & Lim, J. Y. (2017). The prospect of the fourth industrial revolution and home healthcare in super-aged society. Annals of Geriatric Medicine and Research, 21(3), 95–100.CrossRefGoogle Scholar
  13. 13.
    Chomutare, T., Fernandez-Luque, L., Arsand, E., & Hartvigsen, G. (2011). Features of mobile diabetes applications: review of the literature and analysis of current applications compared against evidence-based guidelines. Journal of Medical Internet Research, 13(3), e65.CrossRefGoogle Scholar
  14. 14.
    Jung, H., & Chung, K. (2016). PHR based life health index mobile service using decision support model. Wireless Personal Communications, 86(1), 315–332.CrossRefGoogle Scholar
  15. 15.
    Yoo, H., & Chung, K. (2017). PHR based diabetes index service model using life behavior analysis. Wireless Personal Communications, 93(1), 161–174.CrossRefGoogle Scholar
  16. 16.
    Jung, H., & Chung, K. (2015). Sequential pattern profiling based bio-detection for smart health service. Cluster Computing, 18(1), 209–219.CrossRefGoogle Scholar
  17. 17.
    ISO/IEEE, 11073-20601: health informatics-person health device communication, application profile optimized exchange protocol. http://www.iso.org. Accessed 2 Aug 2018.
  18. 18.
    Song, C. W., Jung, H., & Chung, K. (2017). Development of a medical big-data mining process using topic modeling. Cluster Computing.  https://doi.org/10.1007/s10586-017-0942-0.CrossRefGoogle Scholar
  19. 19.
    Kim, J. H., Kim, J. K., Lee, D. S., & Chung, K. Y. (2014). Ontology driven interactive healthcare with wearable sensors. Multimedia Tools and Applications, 71(2), 827–841.CrossRefGoogle Scholar
  20. 20.
    Althenyan, Q., Yaseen, Q., Jararweh, Y., & Al-Ayyoub, M. (2016). Cloud support for large scale e-healthcare systems. Annals of Wireless Communications, 17(9–10), 503–515.Google Scholar
  21. 21.
    Celdrán, A. H., Pérez, M. G., García Clemente, F. J., & Pérez, G. M. (2017). Preserving patients’ privacy in health scenarios through a multicontext-aware system. Annals of Wireless Communications, 72(9–10), 577–587.Google Scholar
  22. 22.
    Kim, S. H., & Chung, K. (2015). Emergency situation monitoring service using context motion tracking of chronic disease patients. Cluster Computing, 18(2), 747–759.CrossRefGoogle Scholar
  23. 23.
    Sebbak, F., & Benhammadi, F. (2017). Majority-consensus fusion approach for elderly IoT-based healthcare applications. Annals of Wireless Communications, 72(3–4), 157–171.Google Scholar
  24. 24.
    Haghi, M., Thurow, K., & Stoll, R. (2017). Wearable devices in medical internet of things: scientific research and commercially available devices. Journal of Healthcare Informatics Research, 23(1), 4–15.CrossRefGoogle Scholar
  25. 25.
    Chung, K., Kim, J. C., & Park, R. C. (2016). Knowledge-based health service considering user convenience using hybrid Wi-Fi P2P. Information Technology and Management, 17(1), 67–80.CrossRefGoogle Scholar
  26. 26.
    Jung, H., & Chung, K. (2016). Life style improvement mobile service for high risk chronic disease based on PHR platform. Cluster Computing, 19(2), 967–977.CrossRefGoogle Scholar
  27. 27.
    Chung, K., & Park, R. C. (2017). Cloud based U-healthcare Network with QoS guarantee for mobile health service. Cluster Computing.  https://doi.org/10.1007/s10586-017-1120-0.CrossRefGoogle Scholar
  28. 28.
    Yoo, H., & Chung, K. (2017). Heart rate variability based stress index service model using bio-sensor. Cluster Computing.  https://doi.org/10.1007/s10586-017-0879-3.CrossRefGoogle Scholar
  29. 29.
    Chung, K., & Park, R. C. (2016). PHR open platform based smart health service using distributed object group framework. Cluster Computing, 19(1), 505–517.CrossRefGoogle Scholar
  30. 30.
    Kim, J. C., & Chung, K. (2017). Emerging risk forecast system using associative index mining analysis. Cluster Computing, 20(1), 547–558.CrossRefGoogle Scholar
  31. 31.
    Kim, J. C., & Chung, K. (2018). Mining health-risk factors using PHR similarity in a hybrid P2P network. Peer-to-Peer Networking and Applications.  https://doi.org/10.1007/s12083-018-0631-7.CrossRefGoogle Scholar
  32. 32.
    Jung, H., & Chung, K. (2015). Ontology-driven slope modeling for disaster management service. Cluster Computing, 18(2), 677–692.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Jonghun Kim
    • 1
  • Heetae Jang
    • 2
  • Jong Tak Kim
    • 3
  • Hee-Jun Pan
    • 3
  • Roy C. Park
    • 4
  1. 1.Department of Computer SoftwareDaelim UniversityAnyang-siRepublic of Korea
  2. 2.Department of 4th Industrial Revolution Preparing TeamIncheon Business Information TechnoparkIncheonRepublic of Korea
  3. 3.Department of R&D CenterMSU, Inc.IncheonRepublic of Korea
  4. 4.Division of Computer EngineeringDongseo UniversityBusanRepublic of Korea

Personalised recommendations