Skip to main content

Advertisement

SpringerLink
Log in

Real-time Medical Emergency Response System: Exploiting IoT and Big Data for Public Health

  • Transactional Processing Systems
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Healthy people are important for any nation’s development. Use of the Internet of Things (IoT)-based body area networks (BANs) is increasing for continuous monitoring and medical healthcare in order to perform real-time actions in case of emergencies. However, in the case of monitoring the health of all citizens or people in a country, the millions of sensors attached to human bodies generate massive volume of heterogeneous data, called “Big Data.” Processing Big Data and performing real-time actions in critical situations is a challenging task. Therefore, in order to address such issues, we propose a Real-time Medical Emergency Response System that involves IoT-based medical sensors deployed on the human body. Moreover, the proposed system consists of the data analysis building, called “Intelligent Building,” depicted by the proposed layered architecture and implementation model, and it is responsible for analysis and decision-making. The data collected from millions of body-attached sensors is forwarded to Intelligent Building for processing and for performing necessary actions using various units such as collection, Hadoop Processing (HPU), and analysis and decision. The feasibility and efficiency of the proposed system are evaluated by implementing the system on Hadoop using an UBUNTU 14.04 LTS coreTMi5 machine. Various medical sensory datasets and real-time network traffic are considered for evaluating the efficiency of the system. The results show that the proposed system has the capability of efficiently processing WBAN sensory data from millions of users in order to perform real-time responses in case of emergencies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Xing, J. and Zhu, Y., A survey on body area network. Proc. WiCOM, 2009.

  2. Cavallari, R., Martelli, F., Rosini, R., Buratti, C., and Verdone, R, A survey on wireless body area networks: technologies and design challenges. IEEE Commun. Surv. Tutor. 16, No. 3, Third Quarter 2014.

  3. Mahtab Alam, M., and Hamida, E.B., Surveying wearable human assistive technology for life and safety critical applications: standards, challenges, and opportunities. Sensors 14, 2014.

  4. Kushalnagar, N., Montenegro, G. and Schumacher, C., IPv6 over Low-Power Wireless Personal Area Networks (6LoWPANs): overview, assumptions, problem statement, and goals. IETF RFC 4919 2007.

  5. Sai Kiran, M. P. R., Rajalakshmi, P., Bharadwaj, K., and Acharyya, A, Adaptive rule engine based IoT enabled remote healthcare data acquisition and smart transmission system. 2014 I.E. World Forum on Internet of Things (WF-IoT). 2014.

  6. Montenegro, G., Kushalnagar, N., Hui, J. and Culler, D., Transmission of IPv6 packets over IEEE 802.15.4 networks. IETF RFC4944 2007.

  7. Yang, G., Xie, L., Mäntysalo, M., Zhou, S., Pang, Z., Da Xu, L., Kao-Walter, S, Chen, Q.,and Zheng, L-R., A health-IoT platform based on the integration of intelligent packaging, unobtrusive bio-sensor, and intelligent medicine box. IEEE Trans. Indust Inform 10(4) 2014.

  8. Castillejo, P., Martinez, J.-F., Rodriguez-Molina, J., and Cuerva, A., Integration of wearable devices in a wireless sensor network for an E-health application. IEEE Wireless Commun. 20(4):38–49, 2013.

    Article  Google Scholar 

  9. Morak, J., Kumpusch, H., Hayn, D., Modre-Osprian, R., and Schreier, G., Design and evaluation of a telemonitoring concept based on NFC-enabled mobile phones and sensor devices. IEEE Trans. Inf. Technol. Biomed. 16(1):17–23, 2012.

    Article  PubMed  Google Scholar 

  10. Lee, S.-Y., Wang, L.-H., and Fang, Q., A low-power RFID integrated circuits for intelligent healthcare systems. IEEE Trans. Inf. Technol. Biomed. 14(6):1387–1396, 2010.

    Article  PubMed  Google Scholar 

  11. European Commission Information Society, Internet of things in 2020: a roadmap for the future [Online]. Available: http://www.iot-visitthefuture.eu. 2008.

  12. Hande, A., and Cem, E., Wireless sensor networks for healthcare: a survey. Comput. Netw. 54(15):2688–2710, 2010.

    Article  Google Scholar 

  13. National Information Council, Global trends 2025: a transformed world. US Government Printing Office [Online]. Available: http://www.acus.org/publication/global-trends-2025-transformed-world. 2008.

  14. Li, S., Xu, L., and Wang, X., A continuous biomedical signal acquisition system based on compressed sensing in body sensor networks. IEEE Trans. Ind. Informat. 9(3):1764–1771, 2013.

    Article  Google Scholar 

  15. [ONLINE] http://gigaom.com/2011/10/13/internet-of-things-will-have-24-billiondevices-by-2020/.

  16. Mazhar Rathore, M., Ahmad, A., Anand, P. and Rho, S., Urban planning and building smart cities based on the Internet of things using big data analytics, computer networks, In Press, Available online 11 January 2016, ISSN 1389-1286, http://dx.doi.org/10.1016/j.comnet.2015.12.023.

  17. Awais, A., Anand, P., Mazhar Rathore, M., and Chang, H., Smart cyber society: Integration of capillary devices with high usability based on Cyber–physical system. Future Genera. Comput. Syst. http://dx.doi.org/10.1016/j.future.2015.08.004.

  18. Rathore, M. M. U., Paul, A., Ahmad, A., Chen, B. W., Huang, B., and Ji, W., Real-time big data analytical architecture for remote sensing application. IEEE J. Select. Topics Appl. Earth Observ. Remote Sensing 8(10):4610–4621, 2015. doi:10.1109/JSTARS.2015.2424683.

    Article  Google Scholar 

  19. Rathore, M. M., Ahmad, A., Paul, A., and Jeon, G., Efficient graph-oriented smart transportation using internet of things generated big data. 11th Int. Conf. Sign.-Imag. Technol. Internet-Based Syst. (SITIS), Bangkok 2015:512–519, 2015. doi:10.1109/SITIS.2015.121.

    Google Scholar 

  20. Fusco, F., and Deri, L., High speed network traffic analysis with commodity multi-core systems. ACM IMC 2010. 2010.

  21. [Online]. Available: UCI Machine Learning Repository: Diabetes Data Set, “https://archive.ics.uci.edu/ml/datasets/Diabetes,” Accessed on 31 January 2015.

  22. [Online]. Available: UCI Machine Learning Repository: ICU Data Set, https://archive.ics.uci.edu/ml/datasets/ICU,” Accessed on 31 January 2015.

  23. [Online]. Available: WISDM Lab: Dataset, “www.cis.fordham.edu/wisdm/dataset.php,” Accessed on 31 January 2015.

  24. Ramana, V. B., Prasad Babu, M. S., and Venkateswarlu, N. B. A critical comparative study of liver patients from USA and INDIA: an exploratory analysis. Int. J. Comput. Sci. Issues, ISSN :1694-0784. 2012.

  25. Ramana, B.V., Prasad Babu, M. S., and Venkateswarlu, N. B., A critical study of selected classification algorithms for liver disease diagnosis. Int. J. Database Manag. Syst. (IJDMS) 3(2), ISSN : 0975-5705, PP 101-114. 2011.

  26. Attila, R., and Stricker, D., Introducing a new benchmarked dataset for activity monitoring. Wearable Computers (ISWC), 2012 16th Int. Symp. 108-109. IEEE, 2012.

  27. Attila, R., and Stricker, D., Creating and benchmarking a new dataset for physical activity monitoring. Proc. 5th Int. Conf. PErvasive Technol. Relat. Assist. Environ. 40. ACM, 2012.

Download references

Acknowledgments

This study was supported by the Brain Korea 21 Plus project (SW Human Resource Development Program for Supporting Smart Life) funded by the Ministry of Education, School of Computer Science and Engineering, Kyungpook National University, Korea (21A20131600005). This work was also supported by the IT R&D Program of MSIP/IITP. [10041145, Self-Organized Software Platform (SoSp) for Welfare Devices].

Author information

Authors and Affiliations

  1. The School of Computer Science and Engineering, Kyungpook National University, Daegu, 702-701, South Korea

    M. Mazhar Rathore, Awais Ahmad & Anand Paul

  2. The School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, China

    Jiafu Wan

  3. The School of Software Engineering, Tongji University, Tongji, China

    Daqiang Zhang

Authors
  1. M. Mazhar Rathore
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Awais Ahmad
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Anand Paul
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiafu Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Daqiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anand Paul.

Additional information

This article is part of the Topical Collection on Transactional Processing Systems.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rathore, M.M., Ahmad, A., Paul, A. et al. Real-time Medical Emergency Response System: Exploiting IoT and Big Data for Public Health. J Med Syst 40, 283 (2016). https://doi.org/10.1007/s10916-016-0647-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-016-0647-6

Keywords

Search

Navigation