Top

Wireless Personal Communications

Published in:

17-11-2021

IoT for Health Monitoring System Based on Machine Learning Algorithm

Authors: S. Balakrishnan, K. Suresh Kumar, L. Ramanathan, S. K. Muthusundar

Published in: Wireless Personal Communications | Issue 1/2022

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Internet of things (IoT) is one of the futuristic and upcoming technologies which have led to the concept of automation in many daily chores. IoT has placed its fame nearly in all possible platforms and the main idea is to enhance this technology in the current healthcare system, for the purpose of automatic monitoring of hospitals and patient’s health by spreading positive vibes to the vision of IoT. With the support of certain technologies such as Radio Frequency Identification (RFID), Brainsense headband, Wireless Sensor Network and smart mobile, by keeping the IoT as its connecting platform, a Smart E-Healthcare System is framed. A low power wireless personal area connection helps in incorporating these technologies together through a Constrained Application Protocol/IPv6. To track the state of the patient, Smart Healthcare Sensor (SHS) and RFID are used that are attached to the person’s wrist bands automatically by using electromagnetic fields. Each tag held by the person has a unique ID and it contains information that is electronically stored in a separate cloud environment. Based on the various information sensed by the SHS sensors, required prescription is given to the victim in the absence of doctors. This technology represents a significant step in the development of health care sectors. In this paper, we are proposing pro prediction algorithm for detecting the prescription for the patient.

previous article Correction to: Edge Enhancement by Noise Suppression in HSI Color Model of UAV Video with Adaptive Thresholding

next article A Self-Powered, Real-Time, NRF24L01 IoT-Based Cloud-Enabled Service for Smart Agriculture Decision-Making System

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Walsh, S., Linton, J., Grace, R., Marshall, & Knutti, S. (2000). MEMS and MOEMS technology and applications. SPIE—The International Society for Optical Engineering.

Kartsakli, E., Antonopoulos, A., Alonso, L., & Verikoukis, C. (2014). A cloud-assisted random linear network coding medium access control protocol for healthcare applications. Sensors, 14, 4806–4830.CrossRef

Balakrishnan, S., Aravind, K., & Jebaraj, R. A. (2018). A novel approach for tumor image set classification based on multi-manifold deep metric learning. International Journal of Pure and Applied Mathematics, 119(10c), 553–562.

Tennina, S., Santos, M., Mesodiakaki, A., Mekikis, P.-V., Kartsakli, E., Antonopoulos, A., Di Renzo, M., Stavridis, A., Graziosi, F., Alonso, L., & Verikoukis, C. (2016). WSN4QoL: WSNs for remote patient monitoring in e-Health applications. 2016 IEEE International Conference on Communications (ICC). IEEE. https://doi.org/10.1109/ICC.2016.7511597CrossRef

Bazzani, M., Conzon, D., Scalera, A., Spirito, M., & Trainito, C. (2012). Enabling the IoT paradigm in e-health solutions through the VIRTUS middleware. IEEE 11th international conference on trust, security and privacy in computing and communications (TrustCom) (pp. 1954–1959). IEEE.

Benharref, A., & Serhani, M. (2014). Novel cloud and SOA-based framework for e-health monitoring using wireless biosensors. IEEE Journal of Biomedical and Health Informatics, 18(1), 46–55.CrossRef

Milenkovi, A., Otto, C., & Jovanov, E. (2006). Wireless sensor networks for personal health monitoring: Issues and an implementation. Computer Communications, 29(1314), 2521–2533.CrossRef

Paradiso, R., Loriga, G., & Taccini, N. (2005). A wearable health care system based on knitted integrated sensors. IEEE Transactions on Information Technology in Biomedicine, 9(3), 337–344.CrossRef

Page, A., Kocabas, O., Soyata, T., Aktas, M., & Couderc, J.-P. (2014). Cloud-based privacy-preserving remote ECG monitoring and surveillance. Annals of Noninvasive Electrocardiology (ANEC). https://doi.org/10.1111/anec.12204CrossRef

10.

Bui, N., & Zorzi, M. (2011). Health care applications: A solution based on the internet of things. Proceedings of the 4th international symposium on applied sciences in biomedical and communication technologies. ISABEL’11.

11.

Olorode, O., & Nourani, M. (2014). Reducing leakage power in wearable medical devices using memory nap controller. 2014 IEEE dallas circuits and systems conference (DCAS) (pp. 1–4). IEEE.

12.

Sheela, K. G., & Varghese, A. R. (2020). Machine learning based health monitoring system. Materials Today: Proceedings, 24, 1788–1794. https://doi.org/10.1016/j.matpr.2020.03.603CrossRef

13.

Pandey, H., & Prabha, S. (2020). Smart health monitoring system using IOT and machine learning techniques. 2020 sixth international conference on bio signals, images, and instrumentation (ICBSII) (pp. 1–4). IEEE. https://doi.org/10.1109/ICBSII49132.2020.9167660CrossRef

14.

Aldahiri, A., Alrashed, B., & Hussain, W. (2021). Trends in using IoT with machine learning in health prediction system. Forecasting, 3, 181–206. https://doi.org/10.3390/forecast3010012CrossRef

15.

Jebaraj Ratnakumar, A., & Balakrishnan, S. (2018). Machine learning based grape leaf disease detection. Journal of Advanced Research in Dynamic & Control Systems, 10, 775–780.

16.

Rallapalli, H., & Bethelli, P. (2017). IOT based patient monitoring system. International Journal of Computing, Communications & Instrumentation Engineering (IJCCIE), 4(1), 115–118.

17.

Fellini, C., Merenda, M., & Della Corte, F. G. (2012). Battery-less smart RFID tag with sensor capabilities, RFID-technologies and applications (RFID-TA). 2012 IEEE international conference on RFID-technologies and applications (RFID-TA) (pp. 160–164). IEEE.

18.

Spanò, E., Di Pascoli, S., & Iannaccone, G. (2016). Low-power wearable ECG monitoring system for multiple-patient remote monitoring. IEEE Sensors Journal, 16(13), 5452–5462.CrossRef

19.

Hossein, H. R. (2015). SPHMS: Smart patient m-healthcare monitoring system with NFC and IOT. International Journal of Computer Applications Technology and Research, 4(12), 956–959.CrossRef

20.

Hassanalieragh, M., Page, A., Soyata, T., Sharma, G., Aktas, M., Mateos, G., Kantarci, B., & Andreescu, S. (2015). Health monitoring and management using internet-of-things (IoT) sensing with cloud-based processing: Opportunities and challenges. 2015 IEEE international conference on services computing (pp. 285–292). IEEE. https://doi.org/10.1109/SCC.2015.47CrossRef

21.

Pardeshi, V., Sagar, S., Murmurwar, S., & Hage, P. (2017). Health monitoring systems using IoT and raspberry Pi—A review. 2017 international conference on innovative mechanisms for industry applications (ICIMIA 2017) (pp. 134–137). IEEE.CrossRef

Title: IoT for Health Monitoring System Based on Machine Learning Algorithm
Authors: S. Balakrishnan
K. Suresh Kumar
L. Ramanathan
S. K. Muthusundar
Publication date: 17-11-2021
Publisher: Springer US
Published in: Wireless Personal Communications / Issue 1/2022
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI: https://doi.org/10.1007/s11277-021-09335-w

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Other articles of this Issue 1/2022

Speed Control of DC Motor with MRPID Controller in the Presence of Noise

EEG Based Automated Detection of Six Different Eye Movement Conditions for Implementation in Personal Assistive Application

Effect of Nano Potassium Fertilizer on Cucumber Amino Acid Component, Volatile Metabolite Components and GLN Family Genes Expression

An Efficient Security Framework for Trusted and Secure Routing in MANET: A Comprehensive Solution

Transformation of Engineering Education in India Through Student Centric Learning Approach

Secure Tamper-Resistant Electronic Health Record Transaction in Cloud System Via Blockchain