Healthcare monitoring and data analysis using IoT : technologies and applications

Healthcare monitoring and data analysis using IoT : technologies and applications /

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Bibliographic Details
Corporate Author: ProQuest (Firm)
Other Authors: Jain, Vishal, 1983- (Editor), Chatterjee, Jyotir Moy (Editor), Kumar, Pradeep (Editor), Kose, Utku, 1985- (Editor)
Format: Electronic eBook
Language:English
Published: Stevenage : Institution of Engineering and Technology, 2022.
Series:Healthcare technologies
Subjects:
Patient monitoring > Technological innovations.
Medical care > Data processing.
Internet of things > Health aspects.
Big data.
Online Access:Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)
Table of Contents:
  • Machine generated contents note: 1. COVID-19 pandemic analysis using application of AI / Arun Velu
  • 1.1. Introduction
  • 1.2. Literature survey
  • 1.3. Dataset used for analysis
  • 1.4. Various machine learning libraries
  • 1.4.1. NumPy
  • 1.4.2. SciPy
  • 1.4.3. Scikit-learn
  • 1.4.4. Theano
  • 1.4.5. TensorFlow
  • 1.4.6. Keras
  • 1.4.7. Py Torch
  • 1.4.8. Pandas
  • 1.4.9. Matplotlib
  • 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 / Jyotir Moy Chatterje
  • 2.1. Introduction
  • 2.1.1. History of m-health
  • 2.1.2. What is m-health?
  • 2.1.3. Adoption of m-health by various countries
  • 2.1.4. Role of IoT in m-health
  • 2.1.5. M-health to maintain social distancing
  • 2.2. Discussion
  • 2.2.1. M-health to maintain social distancing
  • 2.2.2. Impact of m-health during COVID-19
  • 2.2.3. Global government initiatives on e-health and m-health
  • 2.2.4. Applications of m-health in monitoring health
  • 2.2.5. Benefits of m-health technology
  • 2.2.6. Barriers to m-health
  • 2.2.7. Challenges for m-health technology
  • 2.2.8. Future of m-health
  • 2.3. Conclusion and future work
  • References
  • 3. Analysis of Big Data in electroencephalography (EEC) / 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. analytical study of COVID-19 outbreak / Lajwanti Kishnani
  • 4.1. Introduction
  • 4.2. Review of literature
  • 4.2.1. history of identification and spreading in the world
  • 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 Ytiksel
  • 5.1. Introduction
  • 5.2. Related work
  • 5.3. Proposed method
  • 5.3.1. Hardware
  • 5.3.2. Software
  • 5.3.3. ThingSpeak: an IoT web service
  • 5.3.4. Structure and working principle of the system
  • 5.4. Result and discussion
  • 5.4.1. Room temperature
  • 5.4.2. Humidity
  • 5.4.3. Body temperature
  • 5.4.4. Heart rate
  • 5.4.5. Oxygen saturation
  • 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 / Yiu-Wing Leung
  • 6.1. Introduction
  • 6.1.1. Different versions of IoT
  • 6.1.2. IoMT architecture and framework
  • 6.1.3. Technologies
  • 6.1.4. Sensors used in IoMT
  • 6.2. Security threats in IoMT
  • 6.3. Introduction to COVID-19
  • 6.3.1. Implementation of blockchain in IoMT systems
  • 6.4. Proposed system architecture
  • 6.4.1. IoMT device
  • 6.4.2. Results and discussion
  • 6.5. Conclusion and future scope
  • References
  • 7. IoT-based system for a volumetric estimation of human brain morphological features from magnetic resonance images / Baldzs Gulyds
  • 7.1. Introduction
  • 7.2. Materials and methods
  • 7.2.1. Improved fuzzy C-means (FCM) clustering algorithm for the extraction of ROI
  • 7.2.2. IoT-based system for the extraction of ROI
  • 7.3. Results and discussion
  • 7.4. Conclusion and future scope
  • Acknowledgments
  • References
  • 8. Healthcare monitoring through IoT: security challenges and privacy issues / F.O. Durodola
  • 8.1. Introduction
  • 8.2. IoT applications in personalized healthcare
  • 8.2.1. In-clinic care
  • 8.2.2. Remote monitoring
  • 8.2.3. Blood pressure monitoring
  • 8.2.4. Rehabilitation system
  • 8.2.5. Oxygen saturation monitoring
  • 8.2.6. Wheelchair management
  • 8.2.7. Healthcare solutions using smartphones
  • 8.3. Challenges of IoT in personalized healthcare
  • 8.4. Security of IoT in personalized healthcare
  • 8.4.1. inherited security challenges in the IoT
  • 8.4.2. IoT new security challenges
  • 8.4.3. IoT security requirements
  • 8.5. Privacy
  • 8.5.1. Consent
  • 8.6. Conclusion and future scope
  • References
  • 9. E-health natural language processing / Pardeep Kumar
  • 9.1. Unstructured datasets for E-health NLP research
  • 9.2. Annotation challenges dealing with health-care corpora
  • 9.2.1. Semiautomatic approach for the development of gold standard corpus of medical narratives
  • 9.3. NLP methods that can be adopted to tackle semantics for medical text analysis
  • 9.3.1. Rule-based methods
  • 9.3.2. Machine learning methods
  • 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 / Hua Tianfield
  • 10.1. Introduction
  • 10.2. Healthcare asset management
  • 10.3. Challenges and opportunities in healthcare
  • 10.3.1. Health and safety
  • 10.3.2. Regulatory requirements
  • 10.3.3. Data privacy
  • 10.3.4. Data security
  • 10.3.5. Device security
  • 10.3.6. Equipment interoperability
  • 10.3.7. Resource constraints
  • 10.3.8. Sustainability
  • 10.4. Blockchain: concepts and frameworks
  • 10.4.1. Block structure
  • 10.4.2. Smart contracts
  • 10.4.3. Cryptography and distributed ledger technology
  • 10.4.4. Consensus protocols
  • 10.4.5. Blockchain classification
  • 10.4.6. Blockchain frameworks
  • 10.5. Blockchain of things architecture for healthcare asset management
  • 10.6. Major healthcare application areas
  • 10.6.1. Healthcare records
  • 10.6.2. Device location management
  • 10.6.3. Preventive and predictive analysis
  • 10.6.4. Data visualisation
  • 10.6.5. Forecasting
  • 10.6.6. Assisted living and patient monitoring
  • 10.6.7. Healthcare supply chain management
  • 10.6.8. Acquiring/processing patient's clinical data
  • 10.7. Conclusion and future work
  • References
  • 11. Artificial intelligence: practical primer for clinical research in cardiovascular disease / 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
  • 11.4.1. Image identification
  • 11.4.2. Structured data
  • 11.4.3. Unstructured data
  • 11.4.4. Medical images
  • 11.5. Challenges to implementation
  • 11.6. Conclusion and future work
  • References
  • 12. Deep data analysis for COVID-19 outbreak / K.I. Adenuga
  • 12.1. Introduction to deep data analysis
  • 12.1.1. Data visualization
  • 12.1.2. Descriptive statistics
  • 12.1.3. Predictive modelling
  • 12.1.4. Machine learning
  • 12.1.5. Data reduction
  • 12.1.6. Multivariate analysis
  • 12.1.7. Regression analysis
  • 12.1.8. Data wrangling
  • 12.2. Deep data analysis for COVID-19
  • 12.2.1. Artificial neural networks
  • 12.2.2. Deep neural networks
  • 12.2.3. Generative adversarial networks
  • 12.2.4. Deep belief networks
  • 12.2.5. Convolutional neural network
  • 12.2.6. Recurrent neural network (RNN) - long short-term memory
  • 12.2.7. Modular neural network
  • 12.2.8. Sequence-to-sequence models
  • 12.3. CNN architectures
  • 12.3.1. LeNet
  • 12.3.2. AlexNet
  • 12.3.3. VGGNet 16
  • 12.3.4. Google Net/Inception
  • 12.3.5. ResNets
  • 12.4. Building the neural network
  • 12.4.1. Dataset
  • 12.4.2. Data pre-processing
  • 12.4.3. Train-test split
  • 12.4.4. Data augmentation
  • 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 / P.C. Sherimon
  • 13.1. Introduction
  • 13.2. History of healthcare deep learning
  • 13.3. Deep learning benefits
  • 13.4. Components of deep learning
  • 13.4.1. Generative adversarial networks
  • 13.4.2. Multilayer perceptron
  • 13.4.3. Radial basis network
  • 13.4.4. Recurrent neural networks
  • 13.4.5. Convolutional neural networks
  • 13.5. role of deep learning in healthcare in the future
  • 13.6. Deep learning applications in healthcare
  • 13.6.1. Drug discovery
  • 13.6.2. Medical imaging
  • 13.6.3. Insurance fraud
  • 13.6.4. Alzheimer's disease
  • 13.6.5. Genome
  • 13.7. Conclusion and future work
  • References
  • 14. Intelligent classification of ECG signals using machine learning techniques / Jyotir Moy Chatterjee
  • 14.1. Introduction
  • 14.2. Heart-generated ECG signal
  • 14.3. Filtering parameters least-mean-square algorithm
  • 14.3.1. Updated filter coefficient in normalized least-mean-square (NLMS) algorithm
  • 14.3.2. Improved performance LMS (DENLMS) algorithm delaying normalization inaccuracy
  • 14.3.3. LMS is variant of sign data least-mean-square (SDLMS) 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
  • Contents note continued: 15. survey and taxonomy on mutual interference mitigation techniques in wireless body area networks / P.C. Neelakantan
  • 15.1. Introduction
  • 15.2. Interference issues in WBAN
  • 15.3. Mutual interference mitigation schemes
  • 15.3.1. MAC approach
  • 15.3.2. Transmission power control
  • 15.3.3. Adaptive spectrum allocation
  • 15.3.4. Cooperative scheduling for interference mitigation
  • 15.4. Conclusion and future scope
  • References
  • 16. Predicting COVID cases using machine learning, android, and firebase cloud storage / Utku Kose
  • 16.1. Introduction
  • 16.2. Literature survey
  • 16.3. Implementation and methodology
  • 16.4. Machine learning models
  • 16.4.1. Linear regression
  • 16.4.2. Support vector machine
  • 16.4.3. Random forest
  • 16.4.4. Decision tree
  • 16.5. Introduction to android app
  • 16.6. Result analysis
  • 16.6.1. Odisha analysis
  • 16.6.2. Delhi analysis
  • 16.6.3. Maharashtra analysis
  • 16.7. Conclusion and future work
  • References
  • 17. Technological advancement with artificial intelligence in healthcare / Jyotir Moy Chatterjee
  • 17.1. Introduction
  • 17.1.1. Steps to build a machine learning model
  • 17.1.2. Machine learning terminology
  • 17.1.3. ML algorithms
  • 17.2. Literature review
  • 17.2.1. Applications of machine learning in healthcare
  • 17.3. Disease identification and diagnosis
  • 17.3.1. Heart disease
  • 17.3.2. Diabetes
  • 17.3.3. Liver disease
  • 17.3.4. Dengue disease
  • 17.3.5. Hepatitis disease
  • 17.4. Drug discovery and manufacturing
  • 17.5. Electronic health records
  • 17.6. Disease prediction using machine learning
  • 17.7. Fairness
  • 17.7.1. Fairness in the dataset
  • 17.7.2. Fairness in model or algorithm
  • 17.7.3. Fairness in the metrics/results
  • 17.8. Data analytics role in healthcare
  • 17.8.1. Predictive modeling
  • 17.8.2. Reduction in healthcare costs
  • 17.8.3. Empowering advanced chronic disease prevention
  • 17.9. Deep learning applications in healthcare
  • 17.9.1. Drug discovery
  • 17.9.2. Challenges faced by deep learning applications in healthcare
  • 17.10. Conclusion and future scope
  • References
  • 18. Changing dynamics on the Internet of Medical Things: challenges and opportunities / Pardeep Kumar
  • 18.1. Introduction
  • 18.2. 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 / 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 / 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 / Iman El Mir
  • 21.1. Introduction
  • 21.2. Overview of diseases detection and diagnosis techniques
  • 21.3. Supervised learning models
  • 21.3.1. Deep learning models
  • 21.3.2. Neural networks models
  • 21.3.3. Regression models
  • 21.3.4. Traditional classification models
  • 21.3.5. Probabilistic models
  • 21.4. Unsupervised learning models
  • 21.4.1. Clustering models
  • 21.4.2. One-class classification models
  • 21.4.3. Dimensionality reduction 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.

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