An Energy Efficient Wearable Smart IoT System to Predict Cardiac Arrest

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...

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Vydáno v:Advances in human-computer interaction Ročník 2019; číslo 2019; s. 1 - 21
Hlavní autoři: Ucci, Donald R., Young, Roger, ElSaadany, Yosuf Amr, Majumder, AKM Jahangir Alam
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cairo, Egypt Hindawi Publishing Corporation 01.01.2019
Hindawi
John Wiley & Sons, Inc
Wiley
Témata:
Analytics
Architectural engineering
Big Data
Body temperature
Cardiac arrest
Cardiovascular disease
Cloud computing
Communication
Communications systems
Data processing
Energy efficiency
Energy management
Engineering
Fourier transforms
Health care policy
Heart
Heart rate
International conferences
Internet of Things
Learning algorithms
Machine learning
Patients
Power management
Quality of life
R&D
Research & development
Sensors
Signal processing
Smartphones
Wearable computers
Wearable technology
Wireless networks
ISSN:1687-5893, 1687-5907
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Shrnutí: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.
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ISSN:1687-5893
1687-5907
DOI:10.1155/2019/1507465