Technologies Enduring in Internet of Medical Things (IoMT) for Smart Healthcare System

Authors

  • T. Senthilkumar  Department of Information Technology, Hindusthan Institute of Technology, Coimbatore, Tamil Nadu, India
  • B. Manikandan  Department of Information Technology, Hindusthan Institute of Technology, Coimbatore, Tamil Nadu, India
  • M. Ramya Devi  Department of Computer Science and Engineering, Hindusthan College of Engineering and Technology, Coimbatore, Tamil Nadu, India
  • S. Lokesh  Department of Information Technology, Hindusthan Institute of Technology, Coimbatore, Tamil Nadu, India

Keywords:

Internet of Things, IoT Security, Internet of Medical Things (IoMT), Wireless Sensor Networks

Abstract

Internet of Things (IoT) is a system of connected many physical objects that are easily accessible through the web. The ‘thing’ in IoT might be someone with a monitor that has built-in-sensors, i.e. objects that are assigned an informatics address and have the power to gather and transfer knowledge over a network without manual help or intervention. This embedded technology within the objects helps them to move with internal states or the external atmosphere that successively affects the choices taken. IoT will connect devices embedded in varied systems to the web. Once devices/objects will represent themselves digitally, they'll be controlled from anyplace. The property then helps America capture a lot of knowledge from many places, making certain many ways that of accelerating potency and rising safety and IoT security. IoT is transformational forces that may facilitate corporations improve performance through IoT analytics and IoT Security to deliver higher results. Businesses within the utilities, oil & gas, insurance, producing, transportation, infrastructure and retail sectors will reap the advantages of IoT by creating many educated selections, power-assisted by the torrent of mutual and transactional knowledge at their disposal.

References

  1. Jawbone Inc., "Jawbone fitness trackers," accessed April 2015. Online]. Available: https://jawbone.com/up/trackers.
  2. FitBit Inc., "flex: Wireless activity + sleep wristband," accessed April 2015. Online]. Available: https://www.fitbit.com/flex
  3. Apple Inc., "Apple watch," accessed April 2015. Online]. Available: https://www.apple.com/watch/
  4. A. Pantelopoulos and N. Bourbakis, "A survey on wearable sensor-based systems for health monitoring and prognosis," IEEE Trans. Sys., Man,and Cybernetics, Part C: Applic. and Reviews, vol. 40, no. 1, pp. 1–12,Jan 2010.
  5. D. Son, J. Lee, S. Qiao, R. Ghaffari, J. Kim, J. E. Lee, C. Song, S. J. Kim,D. J. Lee, S. W. Jun, S. Yang, M. Park, J. Shin, K. Do, M. Lee, K. Kang,C. S. Hwang, N. Lu, T. Hyeon, , and D.-H. Kim, "Multifunctional wearable devices for diagnosis and therapy of movement disorders," Nature Nanotechnology, pp. 1–8, 2014.
  6. A. Page, O. Kocabas, T. Soyata, M. Aktas, and J.-P. Couderc, "Cloud-Based Privacy-Preserving Remote ECG Monitoring and Surveillance,"Annals of Noninvasive Electrocardiology (ANEC), 2014. Online]. Available: http://dx.doi.org/10.1111/anec.12204
  7. R. Paradiso, G. Loriga, and N. Taccini, "A wearable health care system based on knitted integrated sensors," IEEE Trans. Info. Tech.in Biomedicine, vol. 9, no. 3, pp. 337–344, Sept 2005.
  8. A. Milenkovi, C. Otto, and E. Jovanov, "Wireless sensor networks for personal health monitoring: Issues and an implementation," Comput. Commun., vol. 29, no. 1314, pp. 2521 – 2533, 2006
  9. A. Benharref and M. Serhani, "Novel cloud and SOA-based framework for E-Health monitoring using wireless biosensors," IEEE Journal of Biomed. and Health Inf., vol. 18, no. 1, pp. 46–55, Jan 2014.
  10. S. Babu, M. Chandini, P. Lavanya, K. Ganapathy, and V. Vaidehi, "Cloud-enabled remote health monitoring system," in Int. Conf. on Recent Trends in Inform. Tech. (ICRTIT), July 2013, pp. 702–707.
  11. C. Rolim, F. Koch, C. Westphall, J. Werner, A. Fracalossi, and G. Salvador,"A cloud computing solution for patient’s data collection in health care institutions," in Second Int. Conf. on eHealth, Telemedicine, and Social Medicine, ETELEMED ’10., Feb 2010, pp. 95–99.
  12. L. Wei, N. Kumar, V. Lolla, E. Keogh, S. Lonardi, C. Ratanamahatana,and H. Van Herle, "A practical tool for visualizing and data mining medical time series," in Proc. 18th IEEE Symposium on Computer-Based Med. Sys., June 2005, pp. .
  13. N. Bui and M. Zorzi, "Health care applications: A solution based on the internet of things," in Proc. of the 4th Int. Symposium on Applied Sciences in Biomed. and Com. Tech., ser. ISABEL ’11. New York, NY, USA: ACM, 2011, pp. 131:1–131:5.
  14. Kumar R, Lokesh S & Ramya Devi, M. (2018), Identifying Camouflaging Adversary in MANET Using Cognitive Agents, Wireless Personal Communication, https://doi.org/10.1007/s11277-018-5378-1.
  15. S. Lokesh, S. Malathy, K. Murugan and, G. Sudhasadasivam (2010), Adaptive Slot Allocation and Bandwidth Sharing for Prioritized Handoff Calls in Mobile Networks, International Journal of Computer Science and Information Security, Vol.8 , 52-57.
  16. S.Lokesh and G.Balakrishnan, "Robust Speech Feature Prediction Using Mel-LPC to Improve Recognition Accuracy", Information Technology Journal, vol. 11, no.11, pp. 1644-1699, 2012.
  17. Lokesh, S., Malarvizhi Kumar, P., Ramya Devi, M. et al. An Automatic Tamil Speech Recognition system by using Bidirectional Recurrent Neural Network with Self-Organizing Map Neural Comput & Applic (2018). https://doi.org/10.1007/s00521-018-3466-5
  18. Lokesh, S. & Devi, M.R. Speech recognition system using enhanced mel frequency cepstral coefficient with windowing and framing method Cluster Comput (2017). https://doi.org/10.1007/s10586-017-144
  19. Kanisha, B., Lokesh, S., Kumar, P.M. et al. Speech recognition with improved support vector machine using dual classifiers and cross fitness validation Pers Ubiquit Comput (2018). https://doi.org/10.1007/s00779-018-1139-0
  20. S.Lokesh and G.Balakrishnan, "Speech Enhancement using Mel-LPC Cepstrum and Vector Quantization for ASR", European Journal of Scientific Research, vol.73,No.2, pp. 202-209, 2012.
  21. Selvaraj, L., and Ganesan, B. (2014) Enhancing speech recognition using improved particle swarm optimization based Hidden Markov Model. Scientific World J. DOI: 10.1155/2014/270576.
  22. S.Lokesh, G.Balakrishnan, S.Malathy, and K.Murugan,Computer Interaction to human through photorealistic facial model for interPprocess communication,. in Computing& Communication& and& Networking& Technologies& (ICCCNT),& 2010& International&Conference&on, 2010, pp. 1-7
  23. Priyan Malarvizhi Kumar, S. Lokesh, R. Varatharajan, Gokulnath Chandra Babu, P. Parthasarathy, Cloud and IoT based disease prediction and diagnosis system for healthcare using Fuzzy neural classifier, Future Generation Computer Systems,2018, https://doi.org/10.1016/j.future.2018.04.036.
  24. Kumar R, Lokesh S & Ramya Devi, M. (2018), Identifying Camouflaging Adversary in MANET Using Cognitive Agents, Wireless Personal Communication, https://doi.org/10.1007/s11277-018-5378-1.
  25. S. Lokesh, S. Malathy, K. Murugan and, G. Sudhasadasivam (2010), Adaptive Slot Allocation and Bandwidth Sharing for Prioritized Handoff Calls in Mobile Networks, International Journal of Computer Science and Information Security, Vol.8 , 52-57.

Downloads

Published

2018-06-30

Issue

Section

Research Articles

How to Cite

[1]
T. Senthilkumar, B. Manikandan, M. Ramya Devi, S. Lokesh, " Technologies Enduring in Internet of Medical Things (IoMT) for Smart Healthcare System, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 5, pp.566-572, May-June-2018.