Advertisement

Cluster Computing

, Volume 22, Supplement 1, pp 1847–1859 | Cite as

Development of healthcare service model using physical information data based on cluster sensing technology

  • Bong-Hyun Kim
  • Jai-Woo OhEmail author
Article

Abstract

Socially vulnerable groups are exposed to more than average risk of safety accidents. In the elderly, safety accidents are increasing due to the aging and disease of the body as the age increases and the degree of injury is serious. As new products and facilities appear, children are increasing the risks and risks as a result children who are vulnerable are exposed to the risk of safety accidents. Therefore, in this paper, we designed and implemented a healthcare based wearable service model for real-time situation recognition of vulnerable groups. Therefore, the base station, which is a relay service equipment, as a single communication line is connect to the Internet. In addition, beacon method, which has less distance measurement error than GPS, is used for position location, and advantage of beacon itself is advantageous for indoor location. Also, the Bluetooth type beacon is applied to provide convenience because it can last up to 2 years when we use coin cell battery. Finally, the wearable type is designed and implemented as a watch type. In the wearable watch type the temperature sensor, the acceleration sensor and the heartbeat sensor are integrated method. Through this, physical information and location information were collected and analyzed from the subjects.

Keywords

Healthcare system Wearable service Cluster sensing Physical information Watch type model 

References

  1. 1.
    Korea Consumer Agency, Consumer Injury Surveillance System. http://www.ciss.go.kr (2017)
  2. 2.
    National Electronic Injury Surveillance System. https://www.cpsc.gov/Research--Statistics/NEISS-Injury-Data (2017)
  3. 3.
    Labraoui, N., Gueroui, M., Sekhri, L.: A risk-aware reputation-based trust management in wireless sensor networks. Wirel. Pers. Commun. 87(3), 1037–1055 (2016)CrossRefGoogle Scholar
  4. 4.
    Zhou, J., Liu, L., Liao, G., Lu, J.: Predictive and fault-tolerant location service in mobile ad hoc networks. Wirel. Pers. Commun. 71(4), 3115–3130 (2013)CrossRefGoogle Scholar
  5. 5.
    Kwon, B., Park, J., Lee, S.: A target position decision algorithm based on analysis of path departure for an autonomous path keeping system. Wirel. Pers. Commun. 83(3), 1843–1865 (2015)CrossRefGoogle Scholar
  6. 6.
    Buyukkaya, E., Abdallah, M., Simon, G.: A survey of peer-to-peer overlay approaches for networked virtual environments. Peer-to-Peer Netw. Appl. 8(2), 276–300 (2015)CrossRefGoogle Scholar
  7. 7.
    Yoo, S., Kim, E., Kim, H.: Exploiting user movement direction and hidden access point for smartphone localization. Wirel. Pers. Commun. 78(4), 1863–1878 (2014)CrossRefGoogle Scholar
  8. 8.
    Noh, C.B., Na, W.: Portable health monitoring systems using wearable devices. Indian J. Sci. Technol. 9(36), 1–5 (2016)Google Scholar
  9. 9.
    BCC Research, Wearable Computing; Technologies, Applications & Global Markets (2014)Google Scholar
  10. 10.
    Kim, D.: Wearable device trends and implications. Rep. Korea Inf. Soc. Dev. Inst. 25(21), 1–26 (2013)Google Scholar
  11. 11.
    Conejar, R.J., Kim, H.K.: Designing U-Healthcare Web Services system. Int. J. Softw. Eng. Appl. 9(3), 209–216 (2015)Google Scholar
  12. 12.
    Won, J.Y.: Proximity sensing based on a dynamic vision sensor for mobile devices. IEEE Ind. Electron. Soc. 62(1), 536–544 (2014)CrossRefGoogle Scholar
  13. 13.
    Wu, Z.G., Dai, H.B.: Gesture recognition based on acceleration sensor. Appl. Mech. Mater. 602–605, 1598–1601 (2014)CrossRefGoogle Scholar
  14. 14.
    Alhussein, M.: Automatic facial emotion recognition using weber local descriptor for e-Healthcare system. Clust. Comput. 19(1), 99–108 (2016)Google Scholar
  15. 15.
    Ma, Y., Zhang, Y., Wan, J., Zhang, D., Pan, Ning: Robot and cloud-assisted multi-modal healthcare system. Clust. Comput. 18(3), 1295–1306 (2015)CrossRefGoogle Scholar
  16. 16.
    Gelogo, Y.E., Kim, H.K.: Integration of mobile computing to ubiquitous healthcare. Softw. Eng. Appl. 9(9), 295–302 (2015)Google Scholar
  17. 17.
    Sung, Y., Jeong, Y.S., Park, J.H.: Beacon-based active media control interface in indoor ubiquitous computing environment. Clust. Comput. 19(1), 547–556 (2016)CrossRefGoogle Scholar
  18. 18.
    Kim, K.H., Jeon, M.Y., Lee, J.Y., Jeong, J.H., Jeong, Gu-Min: A study on the app development using sensor signals from smartphone and smart watch. Adv. Sci. Technol. Lett. 62, 66–69 (2014)CrossRefGoogle Scholar
  19. 19.
    Jung, H., Chung, K.: Sequential pattern profiling based bio-detection for smart health service. Clust. Comput. 18(1), 209–219 (2015)CrossRefGoogle Scholar
  20. 20.
    Kim, J.N., Ryu, M.H., Yang, Y.S., Hong, J.Y.: Estimation of walking direction estimation using a shoe-mounted acceleration sensor. Int. J. Multimed. Ubiquitous Eng. 9(5), 215–222 (2014)CrossRefGoogle Scholar
  21. 21.
    Seo, D.B., Jeon, Y.B., Lee, S.H., Lee, K.H.: Cloud computing for ubiquitous computing on M2 M and IoT environment mobile application. Clust. Comput. 19(2), 1001–1013 (2016)CrossRefGoogle Scholar
  22. 22.
    Sain, M., Chung, W.Y., Lee, H.J.: A personalized healthcare analysis system in ubiquitous environment. Kores Inst. Inf. Commun. Eng. 9(2), 235–243 (2011)Google Scholar
  23. 23.
    Kim, C.M., Kang, G.H., Kim, E.S.: Active spinning training system using complex physiological signals. Korea Contents Soc. 15(7), 591–600 (2015)CrossRefGoogle Scholar
  24. 24.
    Zhang, S., McCullagh, P., Zhang, J., Yu, T.: A smartphone based real-time daily activity monitoring system. Clust. Comput. 17(3), 711–721 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Department of Smart ITU1 UniversityYeongdong-gunKorea
  2. 2.Department of Health ManagementKyungdong UniversityWonju-siKorea

Personalised recommendations