CFP last date
20 January 2025
Reseach Article

A Real Time Patient Monitoring System based on Artificial Neural Fuzzy Inference System (ANFIS)

by Kajal Singh, Divya Sharma, Shipra Aggarwal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 146 - Number 15
Year of Publication: 2016
Authors: Kajal Singh, Divya Sharma, Shipra Aggarwal
10.5120/ijca2016910959

Kajal Singh, Divya Sharma, Shipra Aggarwal . A Real Time Patient Monitoring System based on Artificial Neural Fuzzy Inference System (ANFIS). International Journal of Computer Applications. 146, 15 ( Jul 2016), 22-28. DOI=10.5120/ijca2016910959

@article{ 10.5120/ijca2016910959,
author = { Kajal Singh, Divya Sharma, Shipra Aggarwal },
title = { A Real Time Patient Monitoring System based on Artificial Neural Fuzzy Inference System (ANFIS) },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2016 },
volume = { 146 },
number = { 15 },
month = { Jul },
year = { 2016 },
issn = { 0975-8887 },
pages = { 22-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume146/number15/25475-2016910959/ },
doi = { 10.5120/ijca2016910959 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:50:34.397234+05:30
%A Kajal Singh
%A Divya Sharma
%A Shipra Aggarwal
%T A Real Time Patient Monitoring System based on Artificial Neural Fuzzy Inference System (ANFIS)
%J International Journal of Computer Applications
%@ 0975-8887
%V 146
%N 15
%P 22-28
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Over the last few years there has been tremendous growth in the field of healthcare monitoring systems in hospitals and outside of it. Developing wireless health care monitoring devices employing various technologies has become a keen area of interest in India and as well as in other Nations. This proposed work aims to integrate artificial neural intelligence in domain of healthcare monitoring. Wireless body sensor devices have the ability to reach an advance level of human body monitoring utilizing various transmission and data analytics techniques. Implementation of Artificial Neural Fuzzy Inference Systems (ANFIS) would enable the system to work as a smart healthcare system that decides the priority by itself based on the collected psychological parameters from the sensor nodes. Proposed model describes an e-healthcare monitoring system developed for realizing integration of ANFIS in healthcare monitoring systems. The model consists of sensors to collect vital data from patient’s body which is then transmitted by Wi-Fi to a central HUB where fuzzy logic converts the raw data in linguistic variable which is trained in ANFIS to get the status of patient. The developed system provides the reliable, accurate and real-time accessible data of patients continuously and transmits the vital information using a dedicated communication module in case of emergency.

References
  1. Dima S.M., 2012 Antonopoulos C, Gialelis J, Koubias S, “A network reliability oriented event detection scheme for wireless sensor and actors networks”, IEEE conference fuzzy system
  2. Mehmet R.Yuce, 2010 “Implementation of wireless body area networks for healthcare systems”, Elsevier
  3. Kalyani K.L., Rajeshwari N, Yasaswani K, 2014 “WBAN: A Persuasive area in ubiquitous health care” , vignana university Andhra Pradesh , India IJCSIT internation journal of computer science and information technologies, vol.5(1),60-63
  4. Padmavathi G, Shanmugapriya D and Kalaivani M, 2010 “A study on vehicle detection and tracking using wireless sensor networks”, Scientific Research, Wireless Sensor Network, Vol.1, pp. 173-185
  5. WBAN standard group 2009 http://www.ieee802.org/15/pub/TG6.html,
  6. E. Jovanov, A. Milenkovic, C. Otto, P. Groen, 2005 “A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation” ,Journal of Neuro Engineering and Rehabilitation 2 (6)
  7. C.Billions R. K., Marck P. V., Dadious P, 2015 “Fuzzy inference system wireless body area network architechture simulation for health monitoring”, IEEE International conference December 9-12
  8. Agrawal A.T. and Ashtankar P.S., 2013 “ Adaptive neuro-fuzzy inference system for health monitoring at home”, International Journal of Advanced Science and Technology Vol. 55
  9. Zakrzewski M, Junnilal S, Vehkaojal A, Kailanto H, Vainio A.M., Defee I, Lekkalal J, Vanhala J and Hyttinen J, 2009 “Utilization of wireless sensor network for health monitoring in home environment”, IEEE
  10. Malhi K, Mukhopadhyay S.C.,2015 “ A ZigBee based wearable physiological parameters monitoring system”, IEEE sensor journal, vol 12
  11. Ghanavati S, Abawajy A and Izadi D, 2015 “A congestion control scheme based on fuzzy logic in wireless body area networks”, 14th IEEE conference on network computng and application
  12. Jamthe A,, Mishra A, Agrawal D.P., 2014 “Scheduling schemes for Interference Suppression in Healthcare Sensor Networks”,IEEE ICC
  13. Hamza N, Touati F, Khirji L, 2009 “wireless biomedical system design base on ZigBee technology for autonomous healthcare” ICCCP conference muscat
  14. Chen Y. M., Peng Y.J.,2013 “Energy Efficient Fuzzy Routing Protocol In Wireless Body Area Networks”,vol. 4, ISSN2305-8269
  15. Bhunia S.S., Dhar S.K., Mukherjee N, 2014 “iHealth: A fuzzy approach for provisioning intelligent healthcare system in smart city”, IEEE
  16. Ortiz A.M., Ababnch N, Timmon N, Morrison J, “Adptive Routing For Multihop IEEE 802.15.6 Wireless Body Area Network”, IEEE conferenc
  17. Sudharakumar K B, Dhivya S, Mohanavalli S, Chnder R, 2015 “Cloud Based Fuzzy Healthcare System”, Procedia computer science
  18. Trucu C.E., Trucu C.O., 2013“Internet Of Things As Key Enabler For Sustainable Healthcare Delivery”, Procedia computer science
Index Terms

Computer Science
Information Sciences

Keywords

Patient’s vital signal monitoring artificial neural fuzzy inference system (ANFIS) wireless transmission GSM module.