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An Energy Efficient Wearable Smart IoT System to Predict Cardiac Arrest
Joint Authors
Majumder, AKM Jahangir Alam
ElSaadany, Yosuf Amr
Young, Roger
Ucci, Donald R.
Source
Advances in Human-Computer Interaction
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-21, 21 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-02-12
Country of Publication
Egypt
No. of Pages
21
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Majumder, AKM Jahangir Alam& ElSaadany, Yosuf Amr& Young, Roger& Ucci, Donald R.. 2019. An Energy Efficient Wearable Smart IoT System to Predict Cardiac Arrest. Advances in Human-Computer Interaction،Vol. 2019, no. 2019, pp.1-21.
https://search.emarefa.net/detail/BIM-1118112
Modern Language Association (MLA)
Majumder, AKM Jahangir Alam…[et al.]. An Energy Efficient Wearable Smart IoT System to Predict Cardiac Arrest. Advances in Human-Computer Interaction No. 2019 (2019), pp.1-21.
https://search.emarefa.net/detail/BIM-1118112
American Medical Association (AMA)
Majumder, AKM Jahangir Alam& ElSaadany, Yosuf Amr& Young, Roger& Ucci, Donald R.. An Energy Efficient Wearable Smart IoT System to Predict Cardiac Arrest. Advances in Human-Computer Interaction. 2019. Vol. 2019, no. 2019, pp.1-21.
https://search.emarefa.net/detail/BIM-1118112
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1118112