Enhancement of IoT-Based Smart Hospital System Survey Paper: Research Article

Enhancement of IoT-Based Smart Hospital System Survey Paper: Research Article

Amudha S. (SRM Institute of Science and Technology, India) and Murali M. (SRM Institute of Science and Technology, India)
DOI: 10.4018/978-1-5225-8555-8.ch014

Abstract

In an IoT environment, smart object, an ultimate building block, enables the thing-to-thing communication in a smooth way. Huge numbers of heterogeneous objects are connected with each other for sharing data and resources with less human intervention. Sensor data can be used to provide different features by automation, which causes less manpower and less disturbances to human life. Integrating IoT technologies into healthcare domain is major research area, which provides continuous monitoring of human health condition without any interruption and provides optimal services in emergency cases. The proposed system is embedded with enhanced innovative method to predict future events based on its observations. In this chapter, a new framework for smart healthcare systems is introduced by adding intelligent decision making, data fusion, and prediction algorithms using machine learning concepts.
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Every process is automated in health care industry. In Decision making unit Prediction algorithms acts like heart of the system. Which predict some important rules from their data observation. In paper (Shaoen Wu., Jacob Rendall B., Matthew Smith J., Shangyu Zhu.,& Junhong Xu (2017) several prediction algorithms are explained in smart environment. In reference paper (Haibin Zhang ., Jianpeng Li., Bo Wen., Yijie Xun.,& Jiajia Liu.,(2018) deals with solution of the unified architecture which are not exists in Healthcare industry. By considering this they designed an emergence of the Narrowband IoT (NB-IoT).Using this NB-IoT they connect intelligent things and introduce edge computing to deal with the requirement of latency in medical process .In reference Paper (Joseph Siryani., Bereket Tanju & Timothy Eveleigh J (2017) Machine Learning Decision Support System was introduced in Smart Meter Operations to improve quality of data connection in the interconnected Smart Meter operations. In the paper (Udit Satija.,Barathram Ramkumar., & Sabarimalai Manikandan M(2017) a novel ECG quality aware system was explained in an IoT Scenario for continuous Monitoring of Patient Health Care status. In this paper a light weight real time Signal Quality Aware method was used to classify ECG signal into acceptable and unacceptable class. Implementation also done using Ardino. But this system only used for ECG data analysation not for other parameters.

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