The Internet of Medical Things (IoMT) allows clinicians to monitor patients remotely via a network of wearable or implantable devices. The devices are embedded with software or sensors to enable them to send and receive data via the internet so that healthcare professionals can monitor health data such as vital statistics, metabolic rates or drug delivery regimens, and can provide advice or treatment plans based on this real-world, real-time data. This edited book discusses key IoT technologies that facilitate and enhance this process, such as computer algorithms, network architecture, wireless communications, and network security.
Providing a systemic review of trends, challenges and future directions of IoMT technologies, the book examines applications such as breast cancer monitoring systems, patient-centric systems for handling, tracking and monitoring virus variants, and video-based solutions for monitoring babies. The book discusses machine learning techniques for the management of clinical data and includes security issues such as the use of blockchain technology.
Written by a range of international researchers, this book is a great resource for computer engineering researchers and practitioners in the fields of data mining, machine learning, artificial intelligence and the IoT in the healthcare sector.
Author(s): Subhendu Kumar Pani, Priyadarsan Patra, Gianluigi Ferrari, Radoslava Kraleva, Xinheng Wang
Series: Healthcare Technologies
Publisher: The Institution of Engineering and Technology
Year: 2022
Language: English
Pages: 384
City: London
Contents
About the editors
1. Internet of medical things (IoMT): a systematic review of applications, trends, challenges, and future directions | M.A. Jabbar
1.1 Introduction
1.2 Internet of medical things (IoMT) applications
1.3 Security aspects in IoMT
1.4 Challenges and future directions
1.5 Conclusion
References
2. Non-invasive psycho-physiological driver monitoring through IoT-oriented systems | Luca Davoli, Veronica Mattioli, Sara Gambetta, Laura Belli, Luca Carnevali, Marco Martalo`, Andrea Sgoifo, Riccardo Raheli and Gianluigi Ferrari
2.1 Introduction
2.2 Heterogeneous driver monitoring
2.3 In-vehicle IoT-oriented monitoring architecture
2.4 Experimental performance evaluation
2.5 Conclusions and future works
Acknowledgments
References
3. IoT-based biomedical healthcare approach | Anil Audumbar Pise
3.1 Introduction
3.2 IoT-based healthcare biomedical applications
3.3 IoT-based biomedical communication architecture
3.4 Wireless body area networks
3.5 RFID IoT-based biomedical communication protocol
3.6 Problem statement
3.7 IoT and biomedical healthcare system interconnection
3.8 Examples of IoT-based biomedical healthcare devices
3.9 A summary of associated research
3.10 Conclusion
3.11 Future work
References
4. Impact of world pandemic “COVID-19” and an assessment of world health management and economics | Hemanta Kumar Bhuyan and Subhendu Kumar Pani
4.1 Introduction
4.2 Impact on societies
4.3 Impact of health on the emerging COVID-19 pandemic
4.4 Health in the clinical system
4.5 Role of health disease
4.6 Social setting
4.7 Academy
4.8 Stable, predictable employment
4.9 The impact of the COVID-19 pandemic on firms
4.10 Healthcare, social and economic challenges in Bangladesh
4.11 Conclusions
References
5. Artificial intelligence in healthcare | Deepa Joshi and Anikait Sabharwal
5.1 Introduction
5.2 Healthcare data sources
5.3 Legal and ethical obstacles of artificial intelligence-driven healthcare
5.4 Types of healthcare data
5.5 AI techniques developed for structured and unstructured data
5.6 Major disease areas
5.7 Applications of AI in healthcare system
5.8 Examples of AI used in healthcare
References
6. Blockchain in IoT healthcare: case study | Kripa Elsa Saji, Nisha Aniyan, Renisha P. Salim, Pramod Mathew Jacob, Shyno Sara Sam, Kiran Victor and Remya Prasannan
6.1 Overview
6.2 Existing models to secure IoT healthcare
6.3 Blockchain to secure IoT healthcare
6.4 Conclusions
References
7. Adaptive dictionary-based fusion of multi-modal images for health care applications | Aishwarya Nagasubramanian, Chandrasekaran Bennila Thangammal and Vasantha Pragasam Gladis Pushparathi
7.1 Introduction
7.2 Learning a dictionary
7.3 Experimental setup and analysis
7.4 Overview of fusion scheme
7.5 Simulation results and discussion
7.6 Summary
References
8. Artificial intelligence for sustainable e-Health | Shrikaant Kulkarni
8.1 Introduction
8.2 Reduction in margin of error in healthcare
8.3 EPRs, EHRs, and clinical systems
8.4 Barriers in an EHR system
8.5 Getting over interoperability issues
8.6 Barriers in EHR interoperability
8.7 UK-NHS model: characteristics and enhancements
8.8 MNC/MNE characteristics
8.9 UK-NHS model or the MNC organizational model
8.10 e-Health and AI
8.11 Sustainable healthcare
8.12 Sustainable healthcare in the aftermath of COVID-19
8.13 Sustainability in staff and clinical practice during pandemic
8.14 Broadening health-care facilities at home during the COVID-19 pandemic
8.15 Sustainable development groups
8.16 Futuristic research directions
8.17 Role of AI in diabetes care—a case study
8.18 Artificial intelligence and its area of applications
8.19 Conclusion
References
9. An innovative IoT-based breast cancer monitoring system with the aid of machine learning approach | Bichitrananda Patra, Santosini Bhutia, Trilok Pandey and Lambodar Jena
9.1 Introduction
9.2 Related works
9.3 Proposed solution
9.4 Study and discussion of experimental findings
9.5 Conclusion
References
10. Patient-centric smart health-care systems for handling COVID-19 variants and future pandemics: technological review, research challenges, and future directions | Adarsh Kumar, Saurabh Jain, Keshav Kaushik and Rajalakshmi Krishnamurthi
10.1 Introduction
10.2 Internet of Things (IoT) network for patient-centric health-care system
10.3 Blockchain technology and IoT for patient-centric health-care system
10.4 Cybersecurity and IoT for patient-centric health-care system
10.5 Parallel and distributed computing architecture using IoT network for patient-centric health-care system
10.6 Artificial intelligence and machine learning approaches in IoT for smart health-care system
10.7 Virtualization and IoT in IoT for smart health-care system
10.8 IoT and quantum computing for smart health-care system
10.9 Post-quantum cryptography solutions for futuristic security in smart health-care system
10.10 Drone and robotics operation management using IoT network for smart health-care system
10.11 Conclusion and future scope
References
11. Application of intelligent techniques in health-care sector | Niharika Singh, Richa Choudhary, Thipendra Pal Singh and Anshika Mahajan
11.1 Introduction
11.2 Evolution of AI in health-care informatics
11.3 Healthcare in India
11.4 Health-care dataset
11.5 AI in healthcare
References
12. Managing clinical data using machine learning techniques | V. Diviya Prabha and R. Rathipriya
12.1 Introduction
12.2 Related work
12.3 Clinical data analysis
12.4 Conclusion
References
13. Use of IoT and mobile technology in virus outbreak tracking and monitoring | Marimuthu Narayanan Saravana Kumar, Ravi Bharath, Balasubramani Yogeshwaran, Rajendiran Ranjith and Krishnamoorthy Santhosh Kumar
13.1 Introduction
13.2 IoT in healthcare
13.3 IoT health-care applications
13.4 Benefits
13.5 Challenges
13.6 Use of IoT in virus outbreak and monitoring
13.7 Use of mobile apps in healthcare
13.8 Healthcare IoT for virus pandemic management
13.9 Evolution of healthcare (pandemic)-based IoT
13.10 Increase availability of social networks
13.11 Data privacy and security is a significant concern
13.12 Conclusion
Further reading
14. Video-based solutions for newborn monitoring | Veronica Mattioli, Davide Alinovi, Francesco Pisani, Gianluigi Ferrari and Riccardo Raheli
14.1 Introduction
14.2 Vital signs monitoring
14.3 Video processing systems for neonatal disorder detection
14.4 Seizure detection
14.5 Apnea detection
14.6 Conclusion
References
15. IoT sensor networks in healthcare | Rinki Sharma
List of abbreviations
15.1 Introduction
15.2 Wireless sensor networks (WSN) and Internet of Things (IoT)
15.3 Role of Internet of Things (IoT) sensor networks in healthcare
15.4 Communication technologies for health-care IoT sensor networks
15.5 Challenges in the implementation of H-IoT and related research
15.6 Contemporary technologies to overcome the challenges on IoT sensor networks for healthcare
15.7 Conclusion
References
16. Machine learning for Healthcare 4.0: technologies, algorithms, vulnerabilities, and proposed solutions | Saumya and Bharat Bhushan
16.1 Introduction
16.2 Healthcare 4.0
16.3 Machine learning algorithms
16.4 Machine learning for Healthcare 4.0
16.5 Vulnerabilities for ML in Healthcare 4.0
16.6 ML-based solutions for Healthcare 4.0
16.7 Conclusion
References
17. Big data analytics and data mining for healthcare and smart city applications | Sohit Kummar and Bharat Bhushan
17.1 Introduction
17.2 Theoretical background of smart cities
17.3 Computational infrastructures for smart cities big data analytics
17.4 Mining methods for big data
17.5 Advances in healthcare sector
17.6 Conclusion
References
Index
