Healthcare Monitoring and Data Analysis using IoT: Technologies and applications

Contents
About the editors
Preface
Acknowledgments
1. COVID-19 pandemic analysis using application of AI | Pawan Whig, Rahul Reddy Nadikattu and Arun Velu
1.1 Introduction
1.2 Literature survey
1.3 Dataset used for analysis
1.4 Various machine learning libraries
1.5 Training and testing
1.6 Bias and variance
1.7 Result
1.8 Conclusion
References
2. M-health: a revolution due to technology in healthcare sector | Mayuri Diwakar Kulkarni, Ashish Suresh Awate and Jyotir Moy Chatterje
2.1 Introduction
2.2 Discussion
2.3 Conclusion and future work
References
3. Analysis of Big Data in electroencephalography (EEG) | Sagar Motdhare, Garima Mathur and Ravi Kant
3.1 Introduction
3.2 Methodology
3.3 EEG signal recording
3.4 Activity/action of EEG
3.5 EEG applications
3.6 Mathematical model
3.7 Across the boundaries of small sample sizes
3.8 EEG signal analytics and seizure analysis
3.9 EEG digital video
3.10 EEG data storage and its management
3.11 Big Data in epileptic EEG analysis
3.12 Conclusion
3.13 Future scope
References
4. An analytical study of COVID-19 outbreak | Shipra Gupta, Vijay Kumar, P. Patil and Lajwanti Kishnani
4.1 Introduction
4.2 Review of literature
4.3 Method
4.4 Results
4.5 Discussions
4.6 Precautions
4.7 Conclusions and future scope
Acknowledgment
References
5. IoT-based smart healthcare monitoring system | Hakan Yuksel
5.1 Introduction
5.2 Related work
5.3 Proposed method
5.4 Result and discussion
5.5 Conclusion and future scope
References
6. Development of a secured IoMT device with prioritized medical information for tracking and monitoring COVID patients in rural areas | P.K. Jawahar, K. Indragandhi, G. Kannan and Yiu-Wing Leung
6.1 Introduction
6.2 Security threats in IoMT
6.3 Introduction to COVID-19
6.4 Proposed system architecture
6.5 Conclusion and future scope
References
7. An IoT-based system for a volumetric estimation of human brain morphological features from magnetic resonance images | S.N. Kumar, A. Lenin Fred, L.R. Jonisha Miriam, H. Ajay Kumar, I. Christina Jane, Parasuraman Padmanabhan and Balazs Gulyas
7.1 Introduction
7.2 Materials and methods
7.3 Results and discussion
7.4 Conclusion and future scope
Acknowledgments
References
8. Healthcare monitoring through IoT: security challenges and privacy issues | S.O. Owoeye, A.S. Akinade, K.I. Adenuga and F.O. Durodola
8.1 Introduction
8.2 IoT applications in personalized healthcare
8.3 Challenges of IoT in personalized healthcare
8.4 Security of IoT in personalized healthcare
8.5 Privacy
8.6 Conclusion and future scope
References
9. E-health natural language processing | Saman Hina, Raheela Asif and Pardeep Kumar
9.1 Unstructured datasets for E-health NLP research
9.2 Annotation challenges dealing with health-care corpora
9.3 NLP methods that can be adopted to tackle semantics for medical text analysis
9.4 E-health and Internet of Things (IoT)
9.5 Contributions required from NLP researchers in health-care applications
9.6 Conclusion and future work
References
10. Blockchain of things for healthcare asset management | Sajid Nazir, Mohammad Kaleem, Hassan Hamdoun, Jafar Alzubi and Hua Tianfield
10.1 Introduction
10.2 Healthcare asset management
10.3 Challenges and opportunities in healthcare
10.4 Blockchain: concepts and frameworks
10.5 Blockchain of things architecture for healthcare asset management
10.6 Major healthcare application areas
10.7 Conclusion and future work
References
11. Artificial intelligence: practical primer for clinical research in cardiovascular disease | Shivendra Dubey, Chetan Gupta and Kalpana Rai
11.1 Artificial intelligence
11.2 Traditional statistics versus AI
11.3 Representative algorithms of AI
11.4 Machine power along with big data
11.5 Challenges to implementation
11.6 Conclusion and future work
References
12. Deep data analysis for COVID-19 outbreak | S.O. Owoeye, O.J. Odeyemi, F.O. Durodola and K.I. Adenuga
12.1 Introduction to deep data analysis
12.2 Deep data analysis for COVID-19
12.3 CNN architectures
12.4 Building the neural network
12.5 Neural network architecture
12.6 Other parameters used to configure the neural network
12.7 Model summary
12.8 Metrics used for evaluation
12.9 Results and evaluation
12.10 Conclusion and future scope
References
13. Healthcare system using deep learning | J.B. Shajilin Loret and P.C. Sherimon
13.1 Introduction
13.2 History of healthcare deep learning
13.3 Deep learning benefits
13.4 Components of deep learning
13.5 The role of deep learning in healthcare in the future
13.6 Deep learning applications in healthcare
13.7 Conclusion and future work
References
14. Intelligent classification of ECG signals using machine learning techniques | Kuldeep Singh Kaswan, Anupam Baliyan, Jagjit Singh Dhatterwal, Vishal Jain and Jyotir Moy Chatterjee
14.1 Introduction
14.2 Heart-generated ECG signal
14.3 Filtering parameters least-mean-square algorithm
14.4 Retrieve and classify ECG signals utilizing ML-based techniques
14.5 Artificial neural network (ANN)-based ECG signals
14.6 Classification of ECG signals based fuzzy logic (FL)
14.7 Fourier transform wavelet transforms
14.8 Combination of machine learning and statistical algorithms
14.9 Conclusion and future work
References
15. A survey and taxonomy on mutual interference mitigation techniques in wireless body area networks | Neethu Suman and P.C. Neelakantan
15.1 Introduction
15.2 Interference issues in WBAN
15.3 Mutual interference mitigation schemes
15.4 Conclusion and future scope
References
16. Predicting COVID cases using machine learning, android, and firebase cloud storage | Ritesh Kumar Sinha, Sukant Kishoro Bisoy, Saurabh Kumar, Sai Prasad Sarangi and Utku Kose
16.1 Introduction
16.2 Literature survey
16.3 Implementation and methodology
16.4 Machine learning models
16.5 Introduction to android app
16.6 Result analysis
16.7 Conclusion and future work
References
17. Technological advancement with artificial intelligence in healthcare | Manas Kumar Yogi, Jyotsna Garikipati and Jyotir Moy Chatterjee
17.1 Introduction
17.2 Literature review
17.3 Disease identification and diagnosis
17.4 Drug discovery and manufacturing
17.5 Electronic health records
17.6 Disease prediction using machine learning
17.7 Fairness
17.8 Data analytics role in healthcare
17.9 Deep learning applications in healthcare
17.10 Conclusion and future scope
References
18. Changing dynamics on the Internet of Medical Things: challenges and opportunities | Imtiaz Ali Brohi, Najma Imtiaz Ali and Pardeep Kumar
18.1 Introduction
18.2 The applications of Internet of Things
18.3 Healthcare and Internet of Things
18.4 Security in Internet of Medical Things
18.5 Privacy in Internet of Medical Things
18.6 Perception of trust and risk in IoMT
18.7 Conclusion and future scope
References
19. Internet of Drones (IOD) in medical transport application | G. Prasad, J. Kavya and J. Sahana
19.1 Introduction to unmanned aerial vehicle
19.2 Internet of Things in Industry 5.0
19.3 Applications in medical transport
19.4 Methodology and approach
19.5 Conclusion and future
Acknowledgment
References
20. Blockchain-based Internet of Things (IoT) for healthcare systems: COVID-19 perspective | Anand Sharma, S.R. Biradar, H.K.D. Sarma and N.P. Rana
20.1 Introduction
20.2 IoT in healthcare system
20.3 COVID-19 outbreak
20.4 Blockchain
20.5 Blockchain-based IoT for healthcare systems
20.6 Advantages of proposed system
20.7 Conclusion and future scope
References
21. Artificial intelligence-based diseases detection and diagnosis in healthcare | Said El Kafhali and Iman El Mir
21.1 Introduction
21.2 Overview of diseases detection and diagnosis techniques
21.3 Supervised learning models
21.4 Unsupervised learning models
21.5 Reinforcement learning models
21.6 Summary of some applications for disease diagnosis in healthcare
21.7 Some open research problems
21.8 Conclusions
References
Index