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Advances in Human-Computer Interaction
Volume 2019, Article ID 1507465, 21 pages
https://doi.org/10.1155/2019/1507465
Research Article

An Energy Efficient Wearable Smart IoT System to Predict Cardiac Arrest

1University of South Carolina Upstate, SC, USA
2Miami University, OH 45056, USA

Correspondence should be addressed to AKM Jahangir Alam Majumder; ude.hoimaim@aadmujam

Received 26 October 2018; Accepted 1 January 2019; Published 12 February 2019

Guest Editor: Maurizio Rebaudengo

Copyright © 2019 AKM Jahangir Alam Majumder et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Recently, many people have become more concerned about having a sudden cardiac arrest. With the increase in popularity of smart wearable devices, an opportunity to provide an Internet of Things (IoT) solution has become more available. Unfortunately, out of hospital survival rates are low for people suffering from sudden cardiac arrests. The objective of this research is to present a multisensory system using a smart IoT system that can collect Body Area Sensor (BAS) data to provide early warning of an impending cardiac arrest. The goal is to design and develop an integrated smart IoT system with a low power communication module to discreetly collect heart rates and body temperatures using a smartphone without it impeding on everyday life. This research introduces the use of signal processing and machine-learning techniques for sensor data analytics to identify predict and/or sudden cardiac arrests with a high accuracy.