Edge-of-Things in Personalized Healthcare Support Systems

1st Edition - June 19, 2022
Editors: Rajeswari Sridhar, G. R. Gangadharan, Michael Sheng, Rajan Shankaran
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 0 5 8 5 - 5
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 0 7 0 8 - 8

Edge-of-Things in Personalized Healthcare Support Systems discusses and explores state-of-the-art technology developments in storage and sharing of personal healthcare records i… Read more

Purchase options

AI & BIG DATA

Shaping today's innovations

Save up to 25% on AI & Big Data books, eBooks & Journals

Lead the change today

Institutional subscription on ScienceDirect

Request a sales quote

Edge-of-Things in Personalized Healthcare Support Systems discusses and explores state-of-the-art technology developments in storage and sharing of personal healthcare records in a secure manner that is globally distributed to incorporate best healthcare practices. The book presents research into the identification of specialization and expertise among healthcare professionals, the sharing of records over the cloud, access controls and rights of shared documents, document privacy, as well as edge computing techniques which help to identify causes and develop treatments for human disease. The book aims to advance personal healthcare, medical diagnosis, and treatment by applying IoT, cloud, and edge computing technologies in association with effective data analytics.

Cover image
Title page
Table of Contents
Copyright
List of contributors
Chapter 1. Exploring the dichotomy on opportunities and challenges of smart technologies in healthcare systems
Abstract
1.1 Introduction
1.2 Internet of things in healthcare system
1.3 Cloud computing in healthcare system
1.4 Edge computing in healthcare system
1.5 Artificial intelligence in healthcare system
1.6 Future trends and research challenges in healthcare system
1.7 Conclusion
References
Chapter 2. The architecture of smartness in healthcare
Abstract
2.1 Introduction
2.2 Healthcare
2.3 Technology based smartness
2.4 Security
2.5 Conclusion
References
Chapter 3. Personalized decision support for cardiology based on deep learning: an overview
Abstract
3.1 Introduction
3.2 Big Data in cardiology
3.3 Deep learning
3.4 Deep learning applications in cardiology
3.5 Challenges and limitations
3.6 Summary
References
Chapter 4. Data-driven models for cuffless blood pressure estimation using ECG and PPG signals
Abstract
4.1 Introduction
4.2 Review of data-driven models in the literature
4.3 Methodology
4.4 Results
4.5 Discussion
4.6 Limitations
4.7 Conclusions
References
Chapter 5. A recommendation system for the prediction of drug–target associations
Abstract
5.1 Introduction
5.2 Approach
5.3 Implementation
5.4 Conclusions
References
Chapter 6. Towards building an efficient deep neural network based on YOLO detector for fetal head localization from ultrasound images
Abstract
6.1 Introduction
6.2 Related works
6.3 The dataset
6.4 Object detection
6.5 YOLO family of algorithms
6.6 Proposed methodology
6.7 Experimental outcomes—training
6.8 Results and discussion
6.9 Conclusion
References
Further reading
Chapter 7. FunNet: a deep learning network for the detection of age-related macular degeneration
Abstract
7.1 Introduction
7.2 Background
7.3 Proposed method
7.4 Discussion
7.5 Conclusion
References
Chapter 8. An improved method for automated detection of microaneurysm in retinal fundus images
Abstract
8.1 Introduction
8.2 Results and discussion
8.3 Conclusion
8.4 Future scope
References
Chapter 9. Integration and study of map matching algorithms in healthcare services for cognitive impaired person
Abstract
9.1 Introduction
9.2 Literature review
9.3 Speculative computation cognitive assistant module with trajectory mining methodologies
9.4 Experimental evaluations
9.5 Conclusion
References
Further reading
Chapter 10. Emotion-recognition-based music therapy system using electroencephalography signals
Abstract
10.1 Introduction
10.2 Background
10.3 Proposed method
10.4 Results and discussion
10.5 Conclusion
10.6 Future scope
References
Chapter 11. Feedback context-aware pervasive systems in healthcare management: a Boolean Network approach
Abstract
11.1 Introduction and related works
11.2 A Boolean Control Network-based methodology
11.3 The case study
11.4 The Boolean Control Network system model
11.5 Real-life properties and their mathematical formalization
11.6 Evaluation and conclusions
Acknowledgment
References
Chapter 12. Mental stress detection using a wearable device and heart rate variability monitoring
Abstract
12.1 Introduction
12.2 Related work
12.3 Methodology
12.4 Discussion
12.5 Conclusion
References
Chapter 13. Knowledge discovery and presentation using social media analysis in health domain
Abstract
13.1 Introduction
13.2 Related work
13.3 Coronology: proposed ontology for representing COVID-19 information
13.4 Conclusion
References
Chapter 14. Computationally efficient integrity verification for shared data in cloud storage
Abstract
14.1 Introduction
14.2 Related works
14.3 Proposed work CL-IVS
14.4 Security analysis of CL-IVS
14.5 Performance analysis
14.6 Conclusion and future works
References
Further reading
Chapter 15. Intelligent analysis of multimedia healthcare data using natural language processing and deep-learning techniques
Abstract
15.1 Introduction
15.2 Deep-learning-based methods
15.3 Conclusions and future scope
References
Chapter 16. Measurement of the effects of parks on air pollution in megacities: do parks support health betterment?
Abstract
16.1 Introduction
16.2 Related works
16.3 The proposed method
16.4 Discussion
16.5 Conclusion
References
Chapter 17. Internet of Things use case applications for COVID-19
Abstract
17.1 Introduction
17.2 IoT key role in COVID-19
17.3 Monitoring
17.4 Diagnosing
17.5 Tracing
17.6 Disinfecting
17.7 Vaccinating
17.8 Conclusion
References
Index

Rajeswari Sridhar

Rajeswari Sridhar is currently working as an Associate Professor in the Department of Computer Science and Engineering at the National Institute of Technology, Tiruchirappalli, India. Her research interests include Cloud computing, Natural language processing, Information Retrieval, etc. She has guided 2 PhD students in the areas of Resource provisioning in Cloud computing and Access policies for data sharing through the cloud. She is currently guiding 5 research scholars in similar areas. She has published 70 articles in reputed journals and conferences. She is a member of the IEEE, ACM and CSI.

Affiliations and expertise

Associate Professor, Department of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, India

G. R. Gangadharan

G R Gangadharan is working as an Associate Professor in the National Institute of Technology, Tiruchirappalli, India. His research interests are mainly located on the interface between technological and business perspectives. He has published around 95 publications in the reputed international journals, conferences, and book chapters. He has also edited two books. He has received Ph.D. degree in Information and Communication Technology (2008) from the University of Trento, Trento, Italy and European University Association and an M.S. in Information Technology (2004) from Scuola Superiore Sant’Anna, Pisa, Italy. He is a Senior Member of IEEE and ACM.

Affiliations and expertise

Associate Professor, National Institute of Technology, Tiruchirappalli, India

Michael Sheng

Michael Sheng is a full Professor and Head of Department of Computing at Macquarie University, Sydney, Australia. Before moving to Macquarie University, Michael spent 10 years at School of Computer Science, the University of Adelaide (UoA). Prof. Sheng has more than 400 publications as edited books and proceedings, refereed book chapters, and refereed technical papers in journals and conferences. He is ranked by Microsoft Academic as one of the Top Authors in Services Computing (ranked the 5th of All Time worldwide). He is the recipient of the AMiner Most Influential Scholar Award on IoT (2007-2017), ARC Future Fellowship (2014), Chris Wallace Award for Outstanding Research Contribution (2012), and Microsoft Research Fellowship (2003).

Affiliations and expertise

Professor and Head of Department of Computing, Macquarie University, Sydney, Australia

Rajan Shankaran

Rajan Shankaran is a Senior Lecturer in the Department of Computing at Macquarie University. He leads the 'Security, Communications, and Networks Research Group'. He obtained his PhD in Wireless and Mobile communications from the Western Sydney University (Sydney, Australia). He has previously held an appointment at Western Sydney University. His research focuses on Internetworking security and Quality of service with a special focus on Body Area Networks and Medical Implants, Cognitive Radio, Internet of Things and Vehicular Networks and D2D Communications. He has supervised several Master by Research (MRES) and PhD students to successful completion in these areas. He has published in reputed journals such as IEEE Transactions on Vehicular communications and IEEE Sensors as well as in several prestigious conferences. He has also acted as a reviewer for many SCI journals. He has also served in Technical Program Committees for several conferences in computer networking and security.

Affiliations and expertise

Senior Lecturer, Department of Computing, Macquarie University, Sydney, Australia

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health

Edge-of-Things in Personalized Healthcare Support Systems

Purchase options

Shaping today's innovations

Institutional subscription on ScienceDirect

Rajeswari Sridhar

G. R. Gangadharan

Michael Sheng

Rajan Shankaran