book

The Internet of Medical Things (IoMT)

by R. J. Hemalatha, D. Akila, D. Balaganesh, Anand Paul

March 2022

Intermediate to advanced

336 pages

8h 27m

English

Wiley-Scrivener

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1.1 Introduction1.2 Methodology1.3 Results and Discussion1.4 ConclusionReferences
2.1 Introduction2.2 Access Control–Based Security2.3 System Model2.4 Data Classification2.5 Related Work2.6 ConclusionReferences
3.1 Introduction3.2 Existing Technology and Its Review3.3 Research Design3.4 Results3.5 Discussion3.6 ConclusionReferences
4.1 Introduction4.2 Classes of Brain Tumors4.3 Literature Survey4.4 Methodology4.5 ConclusionReferences
5.1 Introduction5.2 Related Works5.3 Materials and Methods5.4 Results and Discussion5.5 ConclusionReferences
6.1 Introduction6.2 Deep Learning Models6.3 Interpretation of Deep Learning With Biomedical Data6.4 ConclusionReferences

7.1 Introduction7.2 Traditional Paper Method7.3 IoMT7.4 Telemedicine and IoMT7.5 Cyber Security7.6 Materials and Methods7.7 Literature Review7.8 Applications of Electronic Health Records7.9 Results and Discussion7.10 Challenges Ahead7.11 ConclusionReferences
8.1 Introduction8.2 Preferences of the Internet of Things8.3 IoMT Progress in COVID-19 Situations: Presentation8.4 Major Applications of IoMTReferences
9.1 Introduction9.2 IoMT Architecture and Platform9.3 Types of Protocols9.4 Testing Process in IoMT9.5 Issues and Challenges9.6 ConclusionReferences
10.1 Introduction10.2 Proposed System Framework10.3 Experimental Evaluation10.4 Performance Evaluation10.5 ConclusionReferences
11.1 Introduction11.2 Materials and Methods11.3 Results and Discussion11.4 Summary11.5 ConclusionReferences
12.1 Introduction12.2 IoMT in Developing Wearable Health Surveillance System12.3 Vital Parameters That Can Be Monitored Using Wearable Devices12.4 Challenges Faced in Customizing Wearable Devices12.5 ConclusionReferences
13.1 Introduction13.2 Big Data and IoT in the Healthcare Industry13.3 Biomedical Big Data Types13.4 Biomedical Data Acquisition Using IoT13.5 Biomedical Data Management Using IoT13.6 Impact of Big Data and IoMT in Healthcare13.7 Discussions and ConclusionsReferences
14.1 Introduction14.2 Blockchain14.3 Blockchain as a Decentralized Security Framework14.4 Existing Healthcare Data Predictive Analytics Using Soft Computing Techniques in Data Science14.5 Literature Review: Medical Data Security in Cloud Storage14.6 ConclusionReferences
15.1 Introduction15.2 Ancient Medical Record, 1600 BC15.3 Greek Medical Record15.4 Islamic Medical Record15.5 European Civilization15.6 Swedish Health Record System15.7 French and German Contributions15.8 American Descriptions15.9 Beginning of Electronic Health Recording15.10 ConclusionReferences

Content preview from The Internet of Medical Things (IoMT)

10Performance Evaluation of Wearable IoT-Enabled Mesh Network for Rural Health Monitoring

G. Merlin Sheeba¹^* and Y. Bevish Jinila²

¹School of Electrical and Electronics, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, India

²School of Computing, Sathyabama Institute of Science and Technology, Chennai, Tamilnadu, India

Abstract

Wearable Internet of Things (IoT)–enabled biosensors are attaining endless interest day by day. The biosensors are a device made up of transducer, biosensor reader device, and a biological element. The growing healthcare demand and consciousness in elderly people has become one of the important aspects. Due to huge technology growth, the medical treatments in urban and rural areas have accelerated to greater dimensions. In rural region, the elderly are not treated in time or treated reactively. The sensors are in the form of bandages, tattoos, shirts, etc., which allows continuous monitoring of blood pressure, glucose, and other biometric physiological data. To address this issue a point-of-care monitoring unit is developed in rural areas for healthcare and awareness. To enhance the performance of the system, a smart and intelligent mesh backbone is integrated for fast transmission of the critical medical data to a remote health IoT cloud server. By experimental analysis, it can be inferred that the survival rate of the critical patients is 10% better compared to conventional scheme. In addition, the end-to-end delay in data transmission ...