Journal of Medical Systems

, 42:230 | Cite as

Internet of things for knowledge administrations by wearable gadgets

  • Sivakumar KrishnanEmail author
  • S. Lokesh
  • M. Ramya Devi
Mobile & Wireless Health
Part of the following topical collections:
  1. Mobile & Wireless Health


The novel gadgets are associated constantly at a quick phase for the development of Internet of Things (IoT). Wearable gadgets are another gathering development in those available gadgets. The recent method in gadgets is to coordinate with IoT and idea is implementing the remote sensor systems that convey novel encounters in day by day exercises. Here, I exhibit a regular day to day existence application including a Wireless Sensor Networks (WSN) for gaming situation. By using this, the physical factors of sports person are estimated and directed by wearable gadgets to Wireless Sensor Networks. The end goal to incorporate diverse equipment stages and to present an administration situated semantic middleware arrangement hooked on a solitary request also utilization of Enterprise Service Bus (ESB) is introduced as a scaffold to ensure coordination of the distinctive conditions and interoperability. Through proposed method everyone can procure information by introducing framework to fresh client. Those clients would be able to get to the information through a wide assortment of gadgets (cell phones, tablets, and PCs) and working frameworks (Android, Windows, Linux, iOS, and so on). Finally we introduced one case study of football match for monitoring 11 players and acquiring data’s and to predict the real time situation in football ground.


IoT Wearable devices Wireless sensor networks Health monitoring Enterprise service bus 


  1. 1.
    García, M., The Impact of IoT on Economic Growth: A Multifactor Productivity Approach, In Proceedings of the International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, 2015.Google Scholar
  2. 2.
    Visvizi, A., Mazzucelli, C., and Lytras, M., Irregular migratory flows: Towards an ICTs’ enabled integrated framework for resilient urban systems. J. Sci. Technol. Policy Manag. 8:227–242, 2017.CrossRefGoogle Scholar
  3. 3.
    Silva, B. M., Rodrigues, J. J., de la Torre Díez, I., López-Coronado, M., and Saleem, K., Mobile-health: A review of current state. J. Biomed. Inform. 56:265–272, 2015.CrossRefGoogle Scholar
  4. 4.
    Gil, D., Ferrández, A., Mora-Mora, H., and Peral, J., Internet of things: A review of surveys based on context aware intelligent services. Sensors 16:1069, 2016.CrossRefGoogle Scholar
  5. 5.
    Colom, J. F., Mora, H., Gil, D., and Signes-Pont, M. T., Collaborative building of behavioural models based on internet of things. Comput. Electr. Eng. 58:385–396, 2016.CrossRefGoogle Scholar
  6. 6.
    Lanza Calderón, J., Sotres García, P., Sánchez González, L., Galache López, J. A., Santana Martínez, J. R., Gutiérrez Polidura, V., and Muñoz Gutiérrez, L., Managing large amounts of data generated by a Smart City internet of things deployment. Int. J. Semant. Web Inf. Syst.:12, 2016.Google Scholar
  7. 7.
    Llaves, A., Corcho, O., Taylor, P., and Taylor, K., Enabling RDF stream processing for sensor data Management in the Environmental Domain. Int. J. Semant. Web Inf. Syst.:12, 2016.Google Scholar
  8. 8.
    Vujovic´, V., and Maksimovic´, M., Raspberry pi as a sensor web node for home automation. Comput. Electr. Eng. 44:153–171, 2015.CrossRefGoogle Scholar
  9. 9.
    Gilart-Iglesias, V., Mora, H., Pérez-delHoyo, R., and García-Mayor, C., A computational method based on radio frequency technologies for the analysis of accessibility of disabled people in sustainable cities. Sustainability 7:14935–14963, 2015.CrossRefGoogle Scholar
  10. 10.
    Ferrández-Pastor, F. J., Mora-Mora, H., Sánchez-Romero, J. L., Nieto-Hidalgo, M., and García-Chamizo, J. M., Interpreting human activity from electrical consumption data using reconfigurable hardware and hidden Markov models. J. Ambient. Intell. Humaniz. Comput. 8:469–483, 2017.CrossRefGoogle Scholar
  11. 11.
    Devi, G. U., Priyan, M. K., and Gokulnath, C., Wireless camera network with enhanced SIFT algorithm for human tracking mechanism. Int. J. Internet Technol. Secured Trans. 8(2):185–194, 2018.CrossRefGoogle Scholar
  12. 12.
    Chen, R. C., Hsieh, C. F., and Chang, W., Using ambient intelligence to extend network lifetime in wireless sensor networks. J. Ambient. Intell. Humaniz. Comput. 7:777–788, 2016.CrossRefGoogle Scholar
  13. 13.
    Santos, J., Rodrigues, J. J., Silva, B. M., Casal, J., Saleem, K., and Denisov, V., An IoT-based mobile gateway for intelligent personal assistants on mobile health environments. J. Netw. Comput. Appl. 71:194–204, 2016.CrossRefGoogle Scholar
  14. 14.
    Kalem, G., and Turhan, Ç., Mobile technology applications in the healthcare industry for disease management and wellness. Procedia Soc. Behav. Sci. 195:2014–2018, 2015.CrossRefGoogle Scholar
  15. 15.
    Gokulnath, C. B., & Shantharajah, S. P. (2018). An optimized feature selection based on genetic approach and support vector machine for heart disease. Cluster Computing, 1-11.Google Scholar
  16. 16.
    Ma, C. Z.-H., Wong, D. W.-C., Lam, W. K., Wan, A. H.-P., and Lee, W. C.-C., Balance improvement effects of biofeedback systems with state-of-the-art wearable sensors: A systematic review. Sensors 16:434, 2016.CrossRefGoogle Scholar
  17. 17.
    Mandl, K. D., Mandel, J. C., and Kohane, I. S., Driving innovation in health systems through an apps-based information economy. Cell Syst. 1:8–13, 2015.CrossRefGoogle Scholar
  18. 18.
    Baldwin, J. L., Singh, H., Sittig, D. F., and Giardina, T. D., Patient portals and health apps: Pitfalls, promises, and what one might learn from the other. Healthcare, 2016.Google Scholar
  19. 19.
    Fafoutis, X., Janko, B., Mellios, E., Hilton, G., Sherratt, S., Piechocki, R., Craddock, I., SPW-1: A Low-Maintenance Wearable Activity Tracker for Residential Monitoring and Healthcare Applications. In Proceedings of the EAI international conference on wearables in healthcare, Budapest, Hungary,14–15 2016.Google Scholar
  20. 20.
    Mashal, I., Alsaryrah, O., and Chung, T. Y., Testing and evaluating recommendation algorithms in internet of things. J. Ambient. Intell. Humaniz. Comput. 7:889–900, 2016.CrossRefGoogle Scholar
  21. 21.
    Romer, K., Kasten, O., and Mattern, F., Middleware challenges for wireless sensor networks. Mob. Comput. Commun. Rev. 2:6, 2002.Google Scholar
  22. 22.
    Mora, H., Colom, J. F., Gil, D., and Jimeno-Morenilla, A., Distributed computational model for shared processing on cyber-physical system environments. Comput. Commun. 111:68–83, 2017.CrossRefGoogle Scholar
  23. 23.
    Teichmann, D., Kuhn, A., Leonhardt, S., and Walter, M., The MAIN shirt: A textile-integrated magnetic induction sensor array. Sensors 14:1039–1056, 2014.CrossRefGoogle Scholar
  24. 24.
    Weyer, S., Weishaupt, F., Kleeberg, C., Leonhardt, S., and Teichmann, D., RheoStim: Development of an adaptive multi-sensor to prevent venous stasis. Sensors 16:428, 2016.CrossRefGoogle Scholar
  25. 25.
    Muaremi, A. S., Tröster, J., Bexheti, G., Monitor, A., and pilgrims, u., Data collection using smartphones and wearable devices. In proceedings of the 2013 ACM conference on pervasive and ubiquitous computing, Zurich. Switzerland 8–12:679–688, 2013.Google Scholar
  26. 26.
    Arsand, E., Muzny, M., Bradway, M., and Muzik, J., Performance of the first combined smartwatch and smartphone diabetes diary application study. J. Diabetes 9:556–563, 2015.Google Scholar
  27. 27.
    Terroso, M., Freitas, R., and Gabriel, J., Active assistance for senior healthcare: A wearable system for fall detection.In proceedings of the Iberian conference on information systems and technologies (CISTI), Lisboa. Portugal:19–22, 2013.Google Scholar
  28. 28.
    Yang, Z., Zhou, Q., Lei, L., Zheng, K., and Xiang, W., An IoT-cloud based wearable ECG monitoring system for smart healthcare. J. Med. Syst. 40:286, 2016.CrossRefGoogle Scholar
  29. 29.
    Banerjee, A., and Gupta, S. K. S., Analysis of smart Mobile applications for healthcare under dynamic context changes. IEEE Trans. Mob. Comput. 14:904–919, 2015.CrossRefGoogle Scholar
  30. 30.
    Chandra Babu, G., and Shantharajah, S. P., Optimal body mass index cutoff point for cardiovascular disease and high blood pressure. Neural Comput. & Applic.:1–10, 2018.Google Scholar
  31. 31.
    H. Martın, A. M. Bernardos, L. Bergesio, and P. Tarrío, “Analysis of key aspects to manage wireless sensor networks in ambi- ent assisted living environments,” in the 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies (SABEL ‘09), pp. 1–8, 2009.Google Scholar
  32. 32.
    Miorandi, D., Sicari, S., De Pellegrini, F., and Chlamtac, I., Internet of things: Vision, applications and research challenges. Ad Hoc Netw. 10(7):1497–1516, 2012.CrossRefGoogle Scholar
  33. 33.
    Bennebroek, M., Barroso, A., Atallah, L., Lo, B., and Yang, G., Deployment of wireless sensors for remote elderly monitoring. In: Proceedings of the 12th IEEE international conference one-health networking, application and services (Healthcom ‘10), pp. 1–5, 2010.Google Scholar
  34. 34.
    O. Garcia Morchon and H. Baldus, “The ANGEL WSN security architecture,” in 3rd International Conference on Sensor Technologies and Applications, pp. 430–435, 2009.Google Scholar
  35. 35.
    Priyan, M. K., Nath, C. G., Balan, E. V., Prabha, K. R., & Jeyanthi, R. (2015). Desktop phishing attack detection and elimination using TSO program. In Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), 2015 International Conference on (pp. 198-201). IEEE.Google Scholar
  36. 36.
    Wood, A. D., Stankovic, J. A., Virone, G. et al., Context aware wireless sensor networks for assisted living and residential monitoring. IEEE Netw. 22(4):26–33, 2008.CrossRefGoogle Scholar
  37. 37.
    Qixin, W., Wook, S., Xue, L. et al., I-living: An open system architecture for assisted living. In: Proceedings of the IEEE international conference on systems, man and cybernetics (SMC’06), pp. 4268–4275, 2006.Google Scholar
  38. 38.
    Lokesh, S., Malathy, S., Murugan, K., and Sudhasadasivam, G., Adaptive slot allocation and bandwidth sharing for prioritized handoff calls in Mobile networks. Int. J. Comput. Sci. Inform. Sec. 8:52–57, 2010.Google Scholar
  39. 39.
    S. Lokesh, G. Balakrishnan, S. Malathy, and K. Murugan, “Computer Interaction to human through photorealistic facial model for inter-process communication”, in International Conference on Computing Communication and Networking Technologies (ICCCNT), 2010, pp. 1-7.Google Scholar
  40. 40.
    Lokesh, S., Kanisha, B., Nalini, S. et al., Speech to speech interaction system using multimedia tools and partially observable Markov decision process for visually impaired students. Multimed. Tools Appl.:1–20, 2018.
  41. 41.
    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.
  42. 42.
    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. Scholar
  43. 43.
    Kumar, P. M., Lokesh, S., Varatharajan, R., Babu, G. C., and Parthasarathy, P., Cloud and IoT based disease prediction and diagnosis system for healthcare using fuzzy neural classifier. Futur. Gener. Comput. Syst., 2018. Scholar
  44. 44.
    Selvaraj, L., and Ganesan, B., Enhancing speech recognition using improved particle swarm optimization based hidden Markov model. Sci. World J., 2014. Scholar
  45. 45.
    Abdel-Basset, M., El-Shahat, D., and Mirjalili, S., A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem. Futur. Gener. Comput. Syst. 85:129–145, 2018.CrossRefGoogle Scholar
  46. 46.
    Abdel-Basset, M., Manogaran, G., Abdel-Fatah, L., and Mirjalili, S., An improved nature inspired meta-heuristic algorithm for 1-D bin packing problems. Pers. Ubiquit. Comput.:1–16, 2018.Google Scholar
  47. 47.
    Abdel-Basset, M., Manogaran, G., Gamal, A., and Smarandache, F., A hybrid approach of neutrosophic sets and DEMATEL method for developing supplier selection criteria. Des. Autom. Embed. Syst.:1–22, 2018.Google Scholar
  48. 48.
    Abdel-Basset, M., Manogaran, G., Mohamed, M., and Smarandache, F., A novel method for solving the fully neutrosophic linear programming problems. Neural Comput. & Applic.:1–11, 2016.Google Scholar
  49. 49.
    Abdel-Basset, M., Manogaran, G., Fakhry, A. E., and El-Henawy, I., 2-levels of clustering strategy to detect and locate copy-move forgery in digital images. Multimed. Tools Appl.:1–19, 2018.Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Principal - AcademicsRathinam Technical CampusCoimbatoreIndia
  2. 2.Department of Computer Science and EngineeringHindusthan Institute of TechnologyCoimbatoreIndia
  3. 3.Department of Computer Science and EngineeringHindusthan College of Engineering and TechnologyCoimbatoreIndia

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