Real-Time Health Care Monitoring System using IoT

  • Authors

    • Sasippriya Saminathan
    • K Geetha
    2018-04-25
    https://doi.org/10.14419/ijet.v7i2.24.12141
  • IOT, ICT, Personal health care, CoAP, MQTT, Random Forest Algorithm.
  • Abstract

    Incorporation of Information and Communication Technology (ICT) in healthcare industry explored the possibilities to optimize the supply of all the available medical resources and provide reliable, efficient healthcare services to the aged people and patients with physical disabilities and chronic illness. In consumer electronics and growing costs of healthcare a vision of connected e-health has evolved which constitute Personal Health Devices (PHD). Present day mobile devices are capable enough to gather data from various sensors and often play a role in physical fitness gateway and then data are collected in PHDs. The network overhead and suitability of the proposed solution for the different environment is presented which includes the integration of different wireless interfaces with cloud services. The work focuses on adapting the MQTT (Message Queue Telemetry Transport) communication model. This protocol is preferred over CoAP which is one-one protocol because it is one of the lightweight protocols used in TCP/IP and it has the feature of many to many communication models. The evaluation of the work is on a PHD prototype device and feasibility of the solution is discussed. The proposed work is to design and develop a Real-time healthcare monitoring system using IoT which is featured with Random Forest algorithm for heart disease prediction by gathering patient’s data from various PHD sensors and timely alert the caretaker as well as a doctor by sending messages through MQTT. It monitors the patient’s physiological parameters remotely and diagnoses the heart diseases as early as possible.The main motto is to reduce the cost of healthcare and give people the awareness about health and fitness.

     

     

  • References

    1. [1] Chiuchisan,M. Dimian, and U. Street, “Internet of things for E-health: An approch to medical applicationâ€, Department of Computer Scienc , Automation and Electronics , 2015.

      [2] M. PustiÅ¡ek and A. Kos, “Challenges in Wearable Devices based Pervasive Wellbeing Monitoringâ€, 2015.

      [3] Y. Zhang, H. Liu, X. Su, P. Jiang, and D. Wei, “on Smart Phone and Browser / Server Structureâ€, vol. 6, no. 4, pp. 717–738, 2015.

      [4] R. Katake, B. Kute, S. Ranjan, and S. C. Jaiswal, “Survey of Health Monitoring Management Using Internet of Things ( IOT )â€, vol. 5, no. 11, pp. 2013–2016, 2016.

      [5] S. S. Shelke and S. A. Bhosale, “A Survey on Healthcare Monitoring System Using Body Sensor Networkâ€, pp. 10362–10366, 2017.

      [6] R. S. Pramila, “A Survey on Effective In-Home Health Monitoring System A Survey on Effective in-Home Health Monitoring Systemâ€, 2016.

      [7] B. Thaduangta et al., “Monitoring System for Elderlyâ€, pp. 1–6.

      [8] A. Kumbi, P. Naik, K. C. Katti, and K. Kotin, “A Survey Paper on the Internet of Things Based Healthcare Systemâ€, vol. 5, pp. 1–4, 2017.

      [9] J. Gómez, B. Oviedo, and E. Zhuma, “Patient Monitoring System Based on Internet of Thingsâ€, Procedia Comput. Sci, vol. 83, pp. 90–97, 2016.

      [10] S. I. Journal, C. Science, and S. Issue, “An Intelligent Patient Monitoring Through Iot By Mqttâ€, pp. 19–23, 2017.

      [11] V. Pardeshi, S. Sagar, S. Murmurwar, and P. Hage, “Raspberry Pi – A Reviewâ€, no. Iconia, pp. 134–137, 2017.

      [12] M. W. Ahmad, M. Mourshed, and Y. Rezgui, “Trees vs Neurons : Comparison between random forest and ANN for high-resolution prediction of building energy consumptionâ€, vol. 147, pp. 77–89, 2017.

      [13] S. Bharathidason, “Improving Classification Accuracy based on Random Forest Model with Uncorrelated High Performing Treeâ€, vol. 101, no. 13, pp. 26–30, 2014.

      [14] F. Algorithm, S. Sreejith, S. Rahul, and R. C. Jisha, “A Real-Time Patient Monitoring System for Heart Disease Prediction Using Random.â€, pp. 134–137, 2017

      [15] D. L. Gupta, “Performance Analysis of Classification Tree Learning Algorithmsâ€, vol. 55, no. 6, pp. 39–44, 2012.

      [16] Y. Xu, V. Mahendran, and S. Radhakrishnan, “Towards SDN-based Fog Computing : MQTT Broker Virtualization for Effective and Reliable Deliveryâ€, pp. 1–6, 2016.

      [17] D. Choudhary, “Original Research Articlesâ€, vol. 1, no. 5, pp. 37–43, 2012.

      [18] T. Padmapriya and V. Saminadan, “Improving Throughput for Downlink Multi user MIMO-LTE Advanced Networks using SINR approximation and Hierarchical CSI feedbackâ€, International Journal of Mobile Design Network and Innovation- Inderscience Publisher, ISSN : 1744-2850 vol. 6, no.1, pp. 14-23, May 2015.

      [19] S.V.Manikanthan and K.srividhya "An Android based secure access control using ARM and cloud computing", Published in: Electronics and Communication Systems (ICECS), 2015 2nd International Conference on 26-27 Feb.2015,Publisher:IEEE, DOI: 10.1109/ECS.2015.7124833.

  • Downloads

  • How to Cite

    Saminathan, S., & Geetha, K. (2018). Real-Time Health Care Monitoring System using IoT. International Journal of Engineering & Technology, 7(2.24), 484-488. https://doi.org/10.14419/ijet.v7i2.24.12141

    Received date: 2018-04-25

    Accepted date: 2018-04-25

    Published date: 2018-04-25