Intelligent Systems Reference Library 178

Prasant Kumar PattnaikSuneeta MohantySatarupa Mohanty   Editors

Smart Healthcare Analytics in loT Enabled Environment

Intelligent Systems Reference Library

Volume 178

Series Editors

Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, PolandLakhmi C. Jain, Faculty of Engineering and Information Technology, Centre forArtificial Intelligence, University of Technology, Sydney, NSW, Australia;KES International, Shoreham-by-Sea, UK;Liverpool Hope University, Liverpool, UK

The aim of this series is to publish a Reference Library, including novel advancesand developments in all aspects of Intelligent Systems in an easily accessible andwell structured form. The series includes reference works, handbooks, compendia,textbooks, well-structured monographs, dictionaries, and encyclopedias. It containswell integrated knowledge and current information in the field of IntelligentSystems. The series covers the theory, applications, and design methods ofIntelligent Systems. Virtually all disciplines such as engineering, computer science,avionics, business, e-commerce, environment, healthcare, physics and life scienceare included. The list of topics spans all the areas of modern intelligent systemssuch as: Ambient intelligence, Computational intelligence, Social intelligence,Computational neuroscience, Artificial life, Virtual society, Cognitive systems,DNA and immunity-based systems, e-Learning and teaching, Human-centredcomputing and Machine ethics, Intelligent control, Intelligent data analysis,Knowledge-based paradigms, Knowledge management, Intelligent agents,Intelligent decision making, Intelligent network security, Interactive entertainment,Learning paradigms, Recommender systems, Robotics and Mechatronics includinghuman-machine teaming, Self-organizing and adaptive systems, Soft computingincluding Neural systems, Fuzzy systems, Evolutionary computing and the Fusionof these paradigms, Perception and Vision, Web intelligence and Multimedia.

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More information about this series at http://www.springer.com/series/8578

Prasant Kumar Pattnaik •

Suneeta Mohanty • Satarupa MohantyEditors

Smart Healthcare Analyticsin IoT Enabled Environment

123

EditorsPrasant Kumar PattnaikSchool of Computer EngineeringKIIT Deemed to be UniversityBhubaneswar, Odisha, India

Suneeta MohantySchool of Computer EngineeringKIIT Deemed to be UniversityBhubaneswar, Odisha, India

Satarupa MohantySchool of Computer EngineeringKIIT Deemed to be UniversityBhubaneswar, Odisha, India

ISSN 1868-4394 ISSN 1868-4408 (electronic)Intelligent Systems Reference LibraryISBN 978-3-030-37550-8 ISBN 978-3-030-37551-5 (eBook)https://doi.org/10.1007/978-3-030-37551-5

© Springer Nature Switzerland AG 2020This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or partof the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmissionor information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilarmethodology now known or hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in thispublication does not imply, even in the absence of a specific statement, that such names are exempt fromthe relevant protective laws and regulations and therefore free for general use.The publisher, the authors and the editors are safe to assume that the advice and information in thisbook are believed to be true and accurate at the date of publication. Neither the publisher nor theauthors or the editors give a warranty, expressed or implied, with respect to the material containedherein or for any errors or omissions that may have been made. The publisher remains neutral with regardto jurisdictional claims in published maps and institutional affiliations.

This Springer imprint is published by the registered company Springer Nature Switzerland AGThe registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

The edited book aims to bring together leading researchers, academic scientists, andresearch scholars to put forward and share their experiences and research results onall aspects of wireless IoT and analytics for smart healthcare. It also provides apremier interdisciplinary platform for educators, practitioners, and researchers topresent and discuss the most recent innovations, trends, and concerns as well aspractical challenges encountered and solutions adopted in the fields of IoT andanalytics for smart health care. The book is organized into fifteen chapters.

Chapter 1 presents an overview of smart healthcare analytics in IoT-enabledenvironment including its benefits, applications, and challenges.

Chapter 2 focuses on the use of mobile technologies in healthcare service andpresents a selected list of emerging research areas. The review is an attempt tocapture the most recent stage in the development of mobile communications andcomputing toward the domain of IoT in healthcare applications in recent years andidentify the broad research challenges with the hope that it will aid researchers toidentify the evolutionary path of the discipline and prepare their research program.This chapter describes the role of IoT in health care including from its currentapplications, some related projects, and the research issues in detail.

Chapter 3 discusses 5G in IoT. It also discussed some techniques, characteristics,and security challenges that may be faced by the fifth generation when it is used.

Chapter 4 presents a portable device (wearable gear) with its communicationsystem which can be used to measure different health parameters and proper careof the child can be taken accordingly. This chapter brings an attempt and interestamong the researchers to monitor the health of rural children in different“Anganwadi” centers.

Chapter 5 presents an approach to provide secured smart door knocker usingIoT, that will check the details of the person who knocked the door, is authorizedhospital visitor or not.

Chapter 6 presents an application of FCM-based segmentation method followedby an effective fusion rule to study and analyze the progression of Alzheimer’sdisease. Selection of salient features from each of the RGB plane of PET image andelimination of artifacts are done by applying fuzzy C-mean clustering approach.

v

Chapter 7 discusses the application and some of the case studies of machinelearning in various medical fields like diagnosing diseases of brain and heart.

Chapter 8 focuses on the removal of salt and pepper noise from the contaminatedGiemsa-stained blood smear image using probabilistic decision-based averagetrimmed filter (PDBATF). The experiments’ outcomes are recorded and comparedwith recently reported algorithms. The proposed algorithm provides better accuracylevel in terms of peak signal-to-noise ratio, image enhancement ratio, mean absoluteerror, and execution time.

Chapter 9 explains the importance of feature selection and feature creationrelated to biomedical data. It also discusses various methods of feature selectionwith its advantages and disadvantages for biomedical data. The experimental resultof this chapter shows that the prediction accuracy of classifiers to be 100% in mostof the cases. The accuracy of classifiers is much better with selected features andgives accurate results with less time and cost.

Chapter 10 presents a technique for real-time deep learning-based scene imagedetection and segmentation and neural text-to-speech (TTS) synthesis, to detect,classify, and segment images in real-time views and generate their correspondingspeeches.

Chapter 11 focuses on a comparative study of different filter bank approaches interms of classification accuracy using a binary classification BCI competitiondataset which has obtained EEG signals from a single subject. Two fundamentaltypes of filter banks have been used along with their non-overlapping and over-lapping temporal sliding window-based techniques.

Chapter 12 states a new framework based on the denoising stack autoencoderand compressing sampling design. The method enables to solve an optimizationproblem without performing the product of large matrices; instead, it takes theadvantage of the stacked and structure of compressing sampling providing betterperformance than traditional greedy pursuit CS methods. Compressing sensing(CS) has been considered for many real-time applications such as MRI, medicalimaging, remote sensing, signal processing.

Chapter 13 presents an overview of big data framework for analytics of medicaldata. It discusses how does the proper selection of features and application ofmachine learning techniques can lead to a better understanding of diseases throughexperiments.

Chapter 14 presents an electroencephalography(EEG)-based approach for brainactivity analysis on the multimodal face dataset to provide an understanding of thevisual response invoked in the brain upon seeing images of faces (familiar, unfa-miliar, and scrambled faces) and applying computational modeling for classificationalong with the removal/reduction of noise in the given channels.

Chapter 15 aims to present the state-of-the-art research relating to various IoTfeatures, its architecture, security features, and different mechanisms to provide asecure working environment for an IoT system.

We are sincerely thankful to the Almighty to supporting and standing at all timeswith us, whether it is good or tough times and given ways to concede us. Startingfrom the call for chapters till the finalization of chapters, all the editors gave their

vi Preface

contributions amicably, which is a positive sign of significant team works. Theeditors are sincerely thankful to all the members of Springer especiallyProf. Lakhmi C. Jain for providing constructive inputs and allowing an opportunityto edit this important book. We are equally thankful to the reviewers who hails fromdifferent places in and around the globe shared their support and stand firm towardthe quality chapter submission.

Bhubaneswar, India Prasant Kumar PattnaikSuneeta MohantySatarupa Mohanty

Preface vii

About This Book

Healthcare service is a multidisciplinary field that emphasizes on various factorslike financial system, social factors, health technologies, and organizational struc-tures that affect the health care of individuals, family, institutions, organizations,and populations. The goal of healthcare services includes patient safety, timeliness,effectiveness, efficiency, and equity. Health service research evaluates innovationsin various health policies including medicare and medicaid coverage, discrepancyin utilization, and access of care. Smart health care comprises m-health, e-health,electronic resource management, smart and intelligent home services, and medicaldevices. The Internet of Things (IoT) is a system comprising of real-world thingsthat interact and communicate with each other with the help of networking tech-nologies. The wide range of potential applications of IoT includes healthcareservices. IoT-enabled healthcare technologies are suitable for remote healthmonitoring including rehabilitation, assisted ambient living, etc. Healthcare ana-lytics can be applied to the collected data from different areas to improve health carewith minimum expenditure. This edited book is designed to address various aspectsof smart health care to detect and analyze various diseases, the underlyingmethodologies, and their security concerns.

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Key Features

1. Addresses the issues in healthcare services and requirements of analytics.2. Addresses the complete functional framework workflow in IoT-enabled

healthcare technologies.3. Explores basic and high-level concepts, thus serving as a manual for those in the

industry while also helping beginners to understand both basic and advancedaspects of IoT healthcare-related issues.

4. Based on the latest technologies, and covering the major challenges, issues, andadvances in IoT healthcare.

5. Exploring intelligent healthcare and clinical decision support system throughIoT ecosystem and its implications to the real world.

6. Explains concepts of location-aware protocols and decisive mobility in IoThealth care for the betterment of the smarter humanity.

7. Intelligent data processing and wearable sensor technologies in IoT-enabledhealthcare.

8. Exploring human–machine interface and its implications in patient-care systemin IoT healthcare.

9. Exploring security and privacy issues and challenges related to data-intensivetechnologies in healthcare-based Internet of Things.

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Contents

1 Smart Healthcare Analytics: An Overview . . . . . . . . . . . . . . . . . . . 1Suneeta Mohanty, Satarupa Mohanty and Prasant Kumar Pattnaik1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1.1 Internet of Things (IoT) . . . . . . . . . . . . . . . . . . . . . . 21.1.2 IoT for Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.2 Benefits of Smart Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . 31.2.1 Real-Time Reporting and Monitoring . . . . . . . . . . . . 31.2.2 Affordability and End-to-End Connectivity . . . . . . . . . 31.2.3 Data Assortment and Analysis . . . . . . . . . . . . . . . . . . 41.2.4 Remote Medical Assistance . . . . . . . . . . . . . . . . . . . . 4

1.3 Challenges of Smart Healthcare . . . . . . . . . . . . . . . . . . . . . . . 41.3.1 Data Security and Privacy Threats . . . . . . . . . . . . . . . 51.3.2 Multiple Devices and Protocols Integration . . . . . . . . 51.3.3 Data Overload and Accuracy . . . . . . . . . . . . . . . . . . . 51.3.4 Internet Disruptions . . . . . . . . . . . . . . . . . . . . . . . . . 6

1.4 Applications of Smart Healthcare . . . . . . . . . . . . . . . . . . . . . . 61.4.1 Glucose-Level Monitoring . . . . . . . . . . . . . . . . . . . . . 61.4.2 Electrocardiogram (ECG) Monitoring . . . . . . . . . . . . 61.4.3 Blood Pressure Monitoring . . . . . . . . . . . . . . . . . . . . 71.4.4 Wearable Devices . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2 Mobile Communications and Computing: A Broad Reviewwith a Focus on Smart Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . 9Debarshi Kumar Sanyal, Udit Narayana Kar and Monideepa Roy2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92.2 Mobile Communications . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2.1 Common Mobile Wireless Networks . . . . . . . . . . . . . 11

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2.3 Research Areas in Mobile Communications . . . . . . . . . . . . . . 142.3.1 Network-Specific Research Directions . . . . . . . . . . . . 162.3.2 Generic Research Directions . . . . . . . . . . . . . . . . . . . 18

2.4 Mobile Computing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.5 Research Areas in Mobile Computing . . . . . . . . . . . . . . . . . . 232.6 IoT in Smart Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

2.6.1 IoT-Based Healthcare Applications . . . . . . . . . . . . . . 252.6.2 Representative Research Projects on IoT-Based

Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272.6.3 IoT in Healthcare: Open Research Issues . . . . . . . . . . 28

2.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

3 A State of the Art: Future Possibility of 5G with IoTand Other Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35Mohammed Abdulhakim Al-Absi, Ahmed Abdulhakim Al-Absi,Mangal Sain and Hoon Jae Lee3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 353.2 Fifth Generation (5G) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 393.3 10 Things that Have 5G Networks Than 4G . . . . . . . . . . . . . . 403.4 5G NR (New Radio) and How It Works . . . . . . . . . . . . . . . . 413.5 Spectrum in 5G . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423.6 Direct Device-to-Device (D2D) Communication . . . . . . . . . . . 433.7 Nodes and Antenna Transmission . . . . . . . . . . . . . . . . . . . . . 453.8 Application Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453.9 Requirements for 5G Mobile Communications . . . . . . . . . . . . 463.10 5G Security and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . 473.11 Promising Technologies for the 5G . . . . . . . . . . . . . . . . . . . . 493.12 Geographical Condensation of Transmitting Stations

and Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503.13 Multiple Dense Antennas . . . . . . . . . . . . . . . . . . . . . . . . . . . 513.14 Millimeter Waves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523.15 Optical Communication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 533.16 Comparison of 1G to 5G Mobile Technology . . . . . . . . . . . . . 533.17 Reasons Why You Don’t yet Have 5G . . . . . . . . . . . . . . . . . . 57

3.17.1 5G Networks Are Limited in Range . . . . . . . . . . . . . 603.17.2 Some Cities Aren’t on Board . . . . . . . . . . . . . . . . . . 603.17.3 Testing Is Crucial . . . . . . . . . . . . . . . . . . . . . . . . . . . 603.17.4 Spectrum Needs to Be Purchased . . . . . . . . . . . . . . . 613.17.5 It’s Expensive to Roll Out 5G . . . . . . . . . . . . . . . . . . 61

3.18 IoT Healthcare System Architecture . . . . . . . . . . . . . . . . . . . . 613.18.1 IoT Challenges in Healthcare . . . . . . . . . . . . . . . . . . 62

3.19 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

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4 Design Model of Smart “Anganwadi Center” for HealthMonitoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67Sasmita Parida, Suvendu Chandan Nayak, Prasant Kumar Pattnaik,Shams Aijaz Siddique, Sneha Keshri and Piyush Priyadarshi4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 674.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 694.3 IoT Platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 694.4 Proposed Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

4.4.1 Working Principle . . . . . . . . . . . . . . . . . . . . . . . . . . 714.4.2 Hardware Required . . . . . . . . . . . . . . . . . . . . . . . . . . 72

4.5 Simulation and Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 744.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

5 Secured Smart Hospital Cabin Door Knocker Using Internetof Things (IoT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77Lakshmanan Ramanathan, Purushotham Swarnalatha,Selvanambi Ramani, N. Prabakaran, Prateek Singh Phogatand S. Rajkumar5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 775.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 785.3 Proposed Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 795.4 Module Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

5.4.1 User Hardware Module . . . . . . . . . . . . . . . . . . . . . . . 805.4.2 Processing Module . . . . . . . . . . . . . . . . . . . . . . . . . . 82

5.5 Implementation Technologies . . . . . . . . . . . . . . . . . . . . . . . . . 835.5.1 Face Detection and Face Recognition . . . . . . . . . . . . 835.5.2 Base 64 Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 85

5.6 System Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 855.7 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

5.7.1 Computational Time . . . . . . . . . . . . . . . . . . . . . . . . . 865.7.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

5.8 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . 88References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

6 Effective Fusion Technique Using FCM Based SegmentationApproach to Analyze Alzheimer’s Disease . . . . . . . . . . . . . . . . . . . 91Suranjana Mukherjee and Arpita Das6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 916.2 Review Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 946.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

6.3.1 Fuzzy Logic Approach . . . . . . . . . . . . . . . . . . . . . . . 986.3.2 Expert Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . 1006.3.3 Fusion Rule Using PCA Based Weighted

Averaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

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6.4 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1026.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

7 Application of Machine Learning in Various Fieldsof Medical Science . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109Subham Naskar, Patel Dhruv, Satarupa Mohantyand Soumya Mukherjee7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109

7.1.1 KNN (K Nearest Neighbor Classifier) . . . . . . . . . . . . 1107.1.2 Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . 1107.1.3 Regularized Logistic Regression . . . . . . . . . . . . . . . . 1117.1.4 Semi-supervised Learning . . . . . . . . . . . . . . . . . . . . . 1117.1.5 Principal Components Analysis . . . . . . . . . . . . . . . . . 1147.1.6 Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . 1147.1.7 Random Forest Classifier . . . . . . . . . . . . . . . . . . . . . 114

7.2 Application of Machine Learning in Heart Diseases . . . . . . . . 1157.2.1 Case Study-1 to Classify Heart Diseases

Using a Machine Learning Approach . . . . . . . . . . . . . 1157.2.2 Case Study-2 to Predict Cardiac Arrest in Critically Ill

Patients from Machine Learning Score Achievedfrom the Variability of Heart Rate . . . . . . . . . . . . . . . . . . . 117

7.3 Application of Machine Learning Algorithms in DiagnosingDiseases of Brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1207.3.1 Case Study 1: Alzheimer’s Disease . . . . . . . . . . . . . . 1207.3.2 Case Study 2: Detecting Parkinson’s Disease from

Progressive Supranuclear Palsy . . . . . . . . . . . . . . . . . 1227.4 A Brief Approach of Medical Sciences in Other Fields . . . . . . 1237.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

8 Removal of High-Density Impulsive Noise in Giemsa StainedBlood Smear Image Using Probabilistic Decision Based AverageTrimmed Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127Amit Prakash Sen and Nirmal Kumar Rout8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1278.2 Proposed Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

8.2.1 Proposed Average Trimmed Filter . . . . . . . . . . . . . . . 1298.2.2 Proposed Patch Else Average Trimmed Filter . . . . . . . 1318.2.3 Proposed Probabilistic Decision Based Average

Trimmed Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1318.3 Simulation Results and Discussion . . . . . . . . . . . . . . . . . . . . . 1328.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

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9 Feature Selection: Role in Designing Smart Healthcare Models . . . 143Debjani Panda, Ratula Ray and Satya Ranjan Dash9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144

9.1.1 Necessity of Feature Selection . . . . . . . . . . . . . . . . . . 1449.2 Classes of Feature Selection . . . . . . . . . . . . . . . . . . . . . . . . . 145

9.2.1 Brief of Filter Methods . . . . . . . . . . . . . . . . . . . . . . . 1459.2.2 Wrapper Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 1479.2.3 Filter Methods Versus Wrapper Methods . . . . . . . . . . 1489.2.4 Embedded Methods . . . . . . . . . . . . . . . . . . . . . . . . . 149

9.3 Feature Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1519.3.1 Scaling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1519.3.2 Linear Discriminant Analysis . . . . . . . . . . . . . . . . . . 1539.3.3 Principal Component Analysis . . . . . . . . . . . . . . . . . . 1539.3.4 SVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1539.3.5 Random Projection . . . . . . . . . . . . . . . . . . . . . . . . . . 1549.3.6 Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . 154

9.4 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1549.5 Our Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

9.5.1 Workflow Diagram . . . . . . . . . . . . . . . . . . . . . . . . . . 1579.5.2 Data Set Description . . . . . . . . . . . . . . . . . . . . . . . . . 1589.5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

9.6 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . . . . 160References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161

10 Deep Learning-Based Scene Image Detection and Segmentationwith Speech Synthesis in Real Time . . . . . . . . . . . . . . . . . . . . . . . . 163Okeke Stephen and Mangal Sain10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16310.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16410.3 The Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16710.4 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16810.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16810.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170

11 Study of Different Filter Bank Approaches in Motor-ImageryEEG Signal Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173Rajdeep Chatterjee and Debarshi Kumar Sanyal11.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17311.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175

11.2.1 Common Spatial Pattern . . . . . . . . . . . . . . . . . . . . . . 17511.2.2 Filter Bank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17611.2.3 Mixture Bagging Classifier . . . . . . . . . . . . . . . . . . . . 17711.2.4 Differential Evolution . . . . . . . . . . . . . . . . . . . . . . . . 178

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11.3 Proposed Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17911.3.1 Temporal Sliding Window . . . . . . . . . . . . . . . . . . . . 17911.3.2 Proposed DE-based Error Minimization . . . . . . . . . . . 181

11.4 System Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18511.4.1 Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18511.4.2 Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185

11.5 Experimental Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18611.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188

12 A Stacked Denoising Autoencoder Compression SamplingMethod for Compressing Microscopic Images . . . . . . . . . . . . . . . . 191P. A. Pattanaik12.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19112.2 Review Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19212.3 Stacked Denoising Autoencoder Compression Sampling

(SDA-CS) Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19312.4 Experiments and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195

12.4.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19512.4.2 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . 196

12.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19612.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199

13 IoT in Healthcare: A Big Data Perspective . . . . . . . . . . . . . . . . . . . 201Ritesh Jha, Vandana Bhattacharjee and Abhijit Mustafi13.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20113.2 Big Data Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20313.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204

13.3.1 Random Forest Technique . . . . . . . . . . . . . . . . . . . . . 20413.4 Experimental Setup and Dataset Description . . . . . . . . . . . . . . 205

13.4.1 EEG DataSet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20513.5 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20713.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210

14 Stimuli Effect of the Human Brain Using EEG SPM Dataset . . . . . 213Arkajyoti Mukherjee, Ritik Srivastava, Vansh Bhatia, Utkarshand Suneeta Mohanty14.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21314.2 Review of Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . 21414.3 Relation Between Electroencephalography (EEG)

and Magnetoencephalography (MEG) . . . . . . . . . . . . . . . . . . . 215

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14.4 Applications of EEG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21514.4.1 Depth of Anaesthesia . . . . . . . . . . . . . . . . . . . . . . . . 21614.4.2 Biometric Systems . . . . . . . . . . . . . . . . . . . . . . . . . . 21614.4.3 Physically Challenged . . . . . . . . . . . . . . . . . . . . . . . . 21614.4.4 Epilepsy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21714.4.5 Alzheimer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21714.4.6 Brain Death . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21714.4.7 Coma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217

14.5 Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21814.6 Visual Stimuli Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219

14.6.1 EEG Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . 22014.6.2 Visualising the Data . . . . . . . . . . . . . . . . . . . . . . . . . 22114.6.3 Artifact Removal . . . . . . . . . . . . . . . . . . . . . . . . . . . 22214.6.4 Locating the Response Source . . . . . . . . . . . . . . . . . . 222

14.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224

15 Securing the Internet of Things: Current and Future Stateof the Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227Sharmistha Roy, Prashant Pranav and Vandana Bhattacharjee15.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22715.2 Concepts and Basic Characteristics of Internet of Things . . . . . 22915.3 IoT Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23115.4 Security Features and Security Requirements of an IoT

System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23215.5 Security Threats in an IoT System: Current and Future

Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23315.6 Security of IoT Enabled Healthcare System . . . . . . . . . . . . . . 23515.7 Security Mechanisms in an IoT System: Current State

of the Art . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23515.8 Current Research Trends Related to IoT Security . . . . . . . . . . 23715.9 Security Issues and Challenges . . . . . . . . . . . . . . . . . . . . . . . 24415.10 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245

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About the Editors

Prasant Kumar Pattnaik, Ph.D. (Computer Science), fellow IETE, senior memberIEEE is a professor at the School of Computer Engineering, KIIT Deemed to beUniversity, Bhubaneswar, India. He has more than a decade of teaching andresearch experience. He has published numbers of research papers in peer-reviewedinternational journals and conferences. He also published many edited book vol-umes in Springer and IGI Global Publication. His areas of interest include mobilecomputing, cloud computing, cyber security, intelligent systems, and brain–com-puter interface. He is one of the associate editors of Journal of Intelligent and FuzzySystems, IOS Press and Intelligent Systems Book Series Editor of CRC Press,Taylor Francis Group.

Suneeta Mohanty, Ph.D. (Computer Science) is working as an assistant professorat the School of Computer Engineering, KIIT Deemed to be University,Bhubaneswar, India. She has published several research papers in peer-reviewedinternational journals and conferences including IEEE and Springer as well asserves as an organizing chair (SCI-2018). She was appointed in many conferencesas a session chair, reviewer, and track co-chair. Her research area includes cloudcomputing, big data, Internet of Things, and data analytics.

Satarupa Mohanty, Ph.D. (Computer Science) is working as an associate pro-fessor at the School of Computer Engineering, KIIT Deemed to be University,Bhubaneswar, India. She has published several research papers in peer-reviewedinternational journals and conferences. She was appointed in many conferences as asession chair, reviewer, and track co-chair. Her research area includes bioinfor-matics, big data, and Internet of Things.

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Chapter 1Smart Healthcare Analytics:An Overview

Suneeta Mohanty, Satarupa Mohanty and Prasant Kumar Pattnaik

Abstract The goal of healthcare services includes patient safety, timeliness, effec-tiveness, efficiency, and equity. Smart healthcare comprises of m-health, e-health,electronic resource management, smart and intelligent home services and medicaldevices. Internet of Things (IoT) enabled healthcare technologies are suitable forremote healthmonitoringwithminimumexpenditure. This chapter gives an overviewincluding benefits, application, and challenges of smart healthcare analytics in IoTenabled environment.

Keywords Healthcare · Internet of Things (IoT) · Analytics · Security

1.1 Introduction

Healthcare is essentially defined as the improvement or maintenance of health andrelevant facilities through the diagnosis, treatment and prevention of the disease,sickness, injury or mental disorders in people. Physicians and health professionalprovides healthcare services. The integral part of the healthcare industry comprisesof Nursing, medicine, dentistry, optometry, pharmacy, physiotherapy and psychol-ogy. Access to healthcare depends on demography, socioeconomic conditions andhealth policies and may differ across nations, boundaries, communities and indi-viduals. Healthcare systems are meant to address the health requirements of targetpopulations. Healthcare is conventionally considered as an important factor for thewell-being of people around the world. An impelling healthcare system can identifythe irregular health conditions and make diagnoses from time to time. The swiftlyaging populace and the related rise in chronic illness are playing a significant role

S. Mohanty (B) · S. Mohanty · P. K. PattnaikSchool of Computer Engineering, KIIT Deemed to Be University, Bhubaneswar, Indiae-mail: smohantyfcs@kiit.ac.in

S. Mohantye-mail: satarupafcs@kiit.ac.in

P. K. Pattnaike-mail: patnaikprasantfcs@kiit.ac.in

© Springer Nature Switzerland AG 2020P. K. Pattnaik et al. (eds.), Smart Healthcare Analytics in IoT Enabled Environment,Intelligent Systems Reference Library 178,https://doi.org/10.1007/978-3-030-37551-5_1

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in modern healthcare structures, and the demand for resources from hospital bedsto expert medical personnel is increasing at an alarming rate. Evidently, a solutionis needed to curtail the pressure on manual healthcare systems whilst continuing toimplement high-quality care to unstable patients, by using all the technical advance-ment at our disposal. An efficient healthcare system can contribute to a significantpart of a country’s development, economy and industrialization.

1.1.1 Internet of Things (IoT)

In 1999, Kevin Ashton used the term Internet of Things (IoT) for the first time [1–6].Internet of Things (IoT) is a system comprising of real world things that interactand communicate with each other with the help of networking technologies [7, 8].The Internet of Things (IoT) can sense, assemble and transport the data withouthuman intervention over network. IoT uses RFID technology, Sensor technology,Smart technology and Nanotechnology for tagging, sensing, thinking and shrinkingof things respectively [9]. IoT architecture comprises of three layers: Physical layer,Network Layer and Application Layer [10]. The physical layer is responsible forthe collection of data from things with the help of RFID, Bluetooth, 6LoWPANtechnologies and convert them to digital setup. The network layer is responsible fortransmission of data between physical layer and application layer securely in wired,wireless and satellite medium. The application layer represents the top most layer ofIoT architecture and responsible for providing personalised user based applicationas per the requirements. In today’s scenario, there exist many applications of IoT[11, 12] for industry [13] and healthcare [14].

1.1.2 IoT for Healthcare

Consolidation of IoT with healthcare has sharply increased across various specificIoTuse-cases. IoT for healthcare is takingmomentum to address the following issues:

• Making the healthcare accessible to remote area where people are deprived ofgood healthcare service due to several reasons.

• In case of emergency, patients information can be communicated to avoid delayin treatment.

• Reduction of manual patient’s data entry by medical staff so that they can monitorthe cases efficiently.

To make the IoT healthcare system well-organized, functional and successful, itsubiquitous influence needs to be considered. The IoT devices are required to be resis-tant to adverse environmental conditions. A particular use case in which IoT deviceis used by people living in remote and underdeveloped areas and getting involvedin occupations like agriculture and construction activities. A damaged device might

1 Smart Healthcare Analytics: An Overview 3

send inaccurate data. This adds limitations to the usage of such devices and keep-ing out of reach to such people. Certain other factors like humidity, moisture in theair, sweat and direct contact with water affect the connectivity and performance ofIoT devices [15]. To solve this problem, hydrophobic nano-coating solutions can beused to maintain uptime and device reliability across the entire IoT domain. The fullapplication of IoT in the area of healthcare lets medical centers function more com-petently and enables patients to obtain a better course of treatment. With the usage ofa technology-based healthcare method, there are incomparable benefits which couldimprove the efficiency and quality of treatments as well as the health of the patients.

1.2 Benefits of Smart Healthcare

Health service research evaluates innovations in various health policies includingmedicare andmedicaid coverage, discrepancy in utilization and access of care. Smarthealthcare comprises of m-health, e-health, electronic resource management, smartand intelligent home services and medical devices. The Internet of Things (IoT) cansense, assemble and transport data without human intervention over the network.Thus, Internet of Things (IoT) enabled healthcare technologies are suitable for remotehealth monitoring.

1.2.1 Real-Time Reporting and Monitoring

Often, there have been situations where a patient falls extremely sick and by the timean ambulance is arranged and the patient to rushed to the hospital, the situation wors-ens. In case of medical emergency, real-time monitoring can save lives. Real-timemonitoring can be achieved using IoT devices/Applications to collect and transferhealth data like blood sugar and oxygen levels, blood pressure, ECG plots and weightto physician over Internet [16]. These collected data are stored in the cloud for fur-ther action by the authorised personnel regardless of their time and place. A studyconducted via the Center of Connected Health Policy indicates that due to remotepatient monitoring on heart failure patients reduced the readmission rate to 50%.

1.2.2 Affordability and End-to-End Connectivity

In IoT based healthcare system various connectivity protocols likeWi-Fi, Bluetooth,ZigBee etc. are used to automate the patient care workflow. Interoperability, dataflow, machine-to-machine communication features of IoT enabled healthcare systemprovides revolutionary ways of treatment at lower cost. Smart healthcare system

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avoids unnecessary visits by utilizing quality resources and improves the recoverystrategy thus bring down the cost.

1.2.3 Data Assortment and Analysis

To support the real-time application feature of IoT healthcare system, connecteddevices sends large amounts of data in a very short period. To store and managesuch huge amount of data from multiple devices and to analyze the data in real-time,access to cloud is required. All theseworkswill be done over cloud and the authorizedpersonnel will get access to the reports with graphs. Thus, IoT healthcare systemspeed up decision-making with the help of these healthcare analytics irrespective oftime and place. Real-time alerting, monitoring and tracking is possible using IoTwhich makes the medical treatment more efficient.

1.2.4 Remote Medical Assistance

In case of an emergency, a patient can contact a doctor situated at a distant locationvia a smart phone application only. With mobility solutions in healthcare, the doc-tor/physician can instantly check the vitals of the patient and identify the ailment.Besides, numerous healthcare delivery chains that are predicting the manufacture ofmachines which can deliver drugs on the basis of a patient’s prescription and the datarelated to the aliment(s) available on the linked devices. This will act as an impetusto saving money and resources.

1.3 Challenges of Smart Healthcare

Technology has attracted more or less all industries inclusive of finance, business,healthcare, andothers. Intending to revolutionize the treatmentwith a prior andproperdiagnosis the healthcare industry is the right upfront to adopt the advancement in thetechnology. The IoT (Internet of Things) has considerably captured the healthcareindustry in a comparably short period. For instance, due to the connected devices,there is the possibility of allowing older persons to concern the doctor safely in theirplace. It helps doctors to grant the benefit of having recourse with the respectivespecialists worldwide regarding the complex cases. However, every pro have itscons attached to it. Accordingly, any technological advancement comes up withits challenges which have to succeed with proper trafficking. Following are somechallenges associated with its implication for the users of healthcare IoT devices.

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1.3.1 Data Security and Privacy Threats

The privacy and security of storing and handling personal health information throughconnected devices is the fundamental concern for the regulatory of IoT facilities inhealthcare. In real-time, IoT devices capture data and transmit those data throughthe connected environment. However, due to the lack of standardization of the pro-tocols the security issues enter into the picture. Many healthcare industries havethe illusion of storing their sensitive information in a secure and encrypted form,without any inside track over the security of data access point. This creates a remark-able threat which gradually increases with the introduction of new devices in thenetwork. Additionally, there is the consequence of ambiguity come up concerningthe data ownership regulation [17]. These components make the data immenselyinfluenced or harmed by cybercriminals and hackers and ultimately endangering thePersonal Health Information (PHI) of both doctor’s as well as the patent’s.

1.3.2 Multiple Devices and Protocols Integration

For the efficient deployment of IoT in the healthcare industry the principal obstacle isthe integration of multiple heterogeneous devices. To collect the patient’s data, mostmedical equipments have to be interconnected and have to be operated cooperatively[18]. For example, one individual suffering from diabetes can have heart disease aswell. The point of concern is that the heterogeneous equipment has not followed aset of protocol standardization. This insufficiency homogeneity within the medicalequipment scale down the purposeful deployment of IoT in healthcare.

1.3.3 Data Overload and Accuracy

The operational heterogeneity and unambiguousness in the communication standardand protocols cause to happen many complexities in the process of collection of dataand its aggregation. IoT based medical equipments collect a flood of data and takeadvantage to derive the better solution deduce from the patient’s report. Anyhow,extracting the insights from the tremendous data without data experts and refinedanalytics measure is quite challenging. Additionally, the growth of data makes itutmost critical for the physicians and medical specialists to identify the meaningfuland actionable data and to reach to a flawless conclusion. Ultimately this result tothe interference in the decision-making process and gives rise to poor quality result.On top of that, the concern is more problematic with the increase in the number ofconnected devices to the IoT [18].

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1.3.4 Internet Disruptions

When checking the performance of medical IoT software, testing specialists dealwith the load, network bandwidth, latency, and other metrics both for mobile andweb applications. In case of crash under an unexpected load surge, it is unacceptableto have crashed in healthcare IoT. Especially for smart medical devices directlyinvolved in patient care, such as a continuous patient monitoring system or a smartinsulin pump it is completely unacceptable [19].

1.4 Applications of Smart Healthcare

The scope of large number of applications of IoT in healthcare instigate the individ-uals to avail the facility. The application of IoT in a medical centre provides a remotehealthcare services as reducing tracking staff, patients and inventory, ensuring avail-ability of critical hardware, reducing emergency room wait time and enhancing drugmanagement and so on. Following sections addresses the various healthcare applica-tions of remote monitoring of patient, elderly care, remote medication, telemedicineand providing consultancy through smart applications.

1.4.1 Glucose-Level Monitoring

The percentage of diabetic people is increasing day by day. Thus, monitoring theirglucose level on a daily basis is highly required. IoT based healthcare has the capabil-ity of monitoring levels of glucose continuously in a non-invasive way. The patientscan take help of the wearable sensors which has can able to track continuously thehealth parameters and can transfer the collected data to the healthcare providers [20].The tracking device consists of a mobile phone, a blood glucose collector and anIoT-based medical procurement detector which can monitor the level of glucose.

1.4.2 Electrocardiogram (ECG) Monitoring

Electrocardiogram (ECG)monitoring is an essential requirement for heart patient. Inthis type of healthcare monitoring system, wireless transmitter and receiver are usedto track heart rate and the basic rhythm, along with the identification of multifacetedarrhythmias, myocardial ischemia by recording the electrical activity of the heart[20].

1 Smart Healthcare Analytics: An Overview 7

1.4.3 Blood Pressure Monitoring

Blood Pressure (BP) monitoring can be done using wearable sensor device. Thedevice should have a BP apparatus to record the BP and should have Internet basedcommunication to transmit the data for analysis. Blipcare is an example of suchdevice.

1.4.4 Wearable Devices

For the healthcare sector IoT has introduced a number of wearable devices likehearables, ingestible sensors, moodables and healthcare charting which has shapeda comfortable lives for patients. The hearables are new-age hearing tools that haveentirely made over the lifestyle of the people who suffered from hearing issues andhave entirely loosed the interaction with the outer world [21].

1.5 Conclusion

In the arena of Internet, there exists an array of alternative healthcare applications toprovide smart healthcare. With the usage of a technology-based healthcare method,there are incomparable benefits which could improve the efficiency and quality oftreatments aswell as the health of the patients.Modern-day healthcare devices shouldanalyze the collected data to find all possible solutions and should determine theoptimal solution by taking into consideration different priorities and constraints ofthe applications.

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