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Dynamic Bio-sensing Process Design in Mobile Wellness Information System for Smart Healthcare

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Abstract

As the wireless communication technologies of data communication media and biosensors to measure bioengineering signals of a human body are advanced, the wearable mobile computing technology is utilized and is developed in various areas such as personal healthcare, and care-giving for senior citizens and sports activity. This study suggests a customized healthcare service by means of wellness clothing that includes digital yarns and bio sensors. Wellness clothing is utilized to acquire, analyze, and present bio-engineering data including ECG, respiration, acceleration, and body temperature as part of the wellness information system framework. The conventional process configuration of biometric information system performs the fixed process without changing statically and consistently after the system is starting. However, the static configuration of these processes appears the inefficient and inconvenient applications of the mobile wellness information system in the mobile computing environment. This work proposes the dynamic process design and execution method as a way to overcome these inefficient static processes. As well as, it proposes a bio-information framework and service platform applicable for light-weighting mobile systems such as wearable computing, wellness system, etc.

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References

  1. Axisa, F., Schmitt, P. M., Gehin, C., Delhomme, G., McAdams, E., & Dittmar, A. (2005). Flexible technologies and smart clothing for citizen medicine, home healthcare, and disease prevention. IEEE Transactions on Information Technology in Biomedicine, 9(3), 325–336.

    Article  Google Scholar 

  2. Lee, T., & Chung, G. (2010). A life cycle design for wearable computing. In The 7th international conference & Expo on emerging technologies for a smarter world.

  3. Gonzalez, S., Robles, V., Pena, J. M., & Menasalvas, E. Instantiation and adaptation of CRISP-DM to Bioinformatics computational processes.

  4. Hunter, A., Schibeci, D., Hiew, H. L., & Bellgard, M. (2005). Grendel: A bioinformatics Web Service-based architecture for accessing HPC resources. In Proceeding ACSW Frontiers ‘ 05 Proceedings of the 2005 Australasian workshop on grid computing and e-research (Vol. 44, pp. 29–32).

  5. Merelli, E., Armano, G., Cannata, N., Corradini, F., d’Inverno, M., Doms, A., et al. (2006). Agents in bioinformatics, computational and systems biology. In Briefings in Bioinformatics Advance Access (pp. 1–15)

  6. Ozisikyilmaz, B., Narayanan, R., Zambreno, J., Memik, G., & Choudhary, A. (2006). An architectural characterization study of data mining and bioinformatics workloads. In Proceedings of the international symposium on workload characterization (IISWC)

  7. Atwa, A. K., Aboelenine, A. S., Mabrouk, M. S., & Kadah, Y. M. (2006). A new enterprise scale software system for the analysis of the biological data: An enterprise lifeware. In Proceedings of the Cairo international biomedical engineering conference (pp. 1–4)

  8. Zakarya, M., Rahman, I. R., Dilawar, N., & Sadaf, R. (2011). An integrative study on bioinformatics computing concepts, issues and problems. International Journal of Computer Science Issues, 8(6(1)), 330–339.

    Google Scholar 

  9. Pathak, B., Rajesh, S. R., Jain, S., Saxena, A., Mahajan, R., & Joshi, R. (2012). Private cloud initiatives using bioinformatics resources and applications facility (BRAF). International Journal on Cloud computing: Services and Architecture (IJCCSA), 2(6), 25–34.

    Google Scholar 

  10. Brown, D. B., & Badilini, F. (2005). HL7 implementation guide. In Regulated Clinical Research Information Management Technical Committee.

  11. Franklin, S. W., & Rajan, S. W. (2010). Personal area network for biomedical monitoring systems using human body as a transmission medium. International Journal of Bio-science and Bio-technology, 2(2), 23–28.

    Google Scholar 

  12. Han, Y. (2011). Bioworks: A workflow system for automation of bioinformatics analysis processes. International Journal of Bio-science and Bio-technology, 3(4), 59–68.

    Google Scholar 

  13. Kerdprasop, K., & Kerdprasop, N. (2012). Bridging data mining model to the automated knowledge base of biomedical informatics. International Journal of Bio-science and Bio-technology, 4(1), 13–32.

    Google Scholar 

  14. Gibas, C., & Jambeck, P. (2001). Developing bioinformatics computer skills. Sebastopol: O’Reilly Media.

    Google Scholar 

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Authors and Affiliations

  1. Support Center for Field Practice Education, Wonkwang WINNER LINC Center, WonKwang University, #460 Iksan-daero, Iksan City, Jeonbuk, 570-749, Korea

    Tae-gyu Lee

  2. School of Information and Communication, BaekSeok University, Cheonan, 330-704, Korea

    Seong-Hoon Lee

Authors
  1. Tae-gyu Lee
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  2. Seong-Hoon Lee
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Correspondence to Tae-gyu Lee.

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Lee, Tg., Lee, SH. Dynamic Bio-sensing Process Design in Mobile Wellness Information System for Smart Healthcare. Wireless Pers Commun 86, 201–215 (2016). https://doi.org/10.1007/s11277-015-2967-0

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  • DOI: https://doi.org/10.1007/s11277-015-2967-0

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