Real Time Patient Vital Monitoring and Alarm System with Prediction of Anomalies and Future Clinical Episodes using Machine Learning Models

Authors

  • Arko Pal Pal Department of Computer Science PES University Bangalore, India
  • Kunal Mujoo Department of Computer Science PES University Bangalore, India
  • Ruby Dinakar Department of Computer Science PES University Bangalore, India
  • Khushi Gupta Department of Computer Science PES University Bangalore, India
  • Akarsh Siwach Department of Computer Science PES University Bangalore, India

DOI:

https://doi.org/10.37934/aram.109.1.7383

Keywords:

Real time vital monitoring, wearable devices, Machine Learning, disease prediction

Abstract

Heart disease is a leading cause of death in India. Many times, heart diseases do not show symptoms until they become a matter of concern. This silent nature calls for real time monitoring of vitals. In this paper, we propose a healthcare monitoring system which makes use of existing wearable devices to measure vitals of the user’s body. The data gathered is used to perform real time analysis to detect any irregularities and simultaneously to predict if there are any underlying problems that might be of future concern.

Author Biographies

Arko Pal Pal, Department of Computer Science PES University Bangalore, India

arko.pal2000@gmail.com

Kunal Mujoo, Department of Computer Science PES University Bangalore, India

mujoo.kunal@gmail.com

Ruby Dinakar, Department of Computer Science PES University Bangalore, India

rubydinakar@pes.edu

Khushi Gupta, Department of Computer Science PES University Bangalore, India

khushigupta515@gmail.com

Akarsh Siwach, Department of Computer Science PES University Bangalore, India

akarshsiwach10@gmail.com

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Published

2023-10-18

How to Cite

Arko Pal Pal, Kunal Mujoo, Ruby Dinakar, Khushi Gupta, & Akarsh Siwach. (2023). Real Time Patient Vital Monitoring and Alarm System with Prediction of Anomalies and Future Clinical Episodes using Machine Learning Models. Journal of Advanced Research in Applied Mechanics, 109(1), 73–83. https://doi.org/10.37934/aram.109.1.7383

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Section

Articles