Internet of things based smart healthcare : intelligent and secure solutions applying machine learning techniques / / Suparna Biswas [and three others] editors

• Pubblicazione: Singapore : , : Springer, , [2022]; ©2022
• Descrizione fisica: 1 online resource (394 pages)
• Disciplina: 004.678
• Soggetto topico: Internet of things; Medical innovations; Medicine - Data processing; Internet de les coses; Processament de dades; Medicina
• Soggetto genere / forma: Llibres electrònics
• Persona (resp. second.): BiswasSuparna
• Nota di bibliografia: Includes bibliographical references.
• Nota di contenuto: Intro -- Contents -- IoT Based Smart Healthcare -- Wearable Sensors and Machine Intelligence for Smart Healthcare -- 1 Introduction -- 2 Presents Healthcare Issues and Challenges for Remote Patients -- 3 The Possibilities of IoT as an Alternate to Conventional Healthcare System -- 4 IoT-Enabled Healthcare System -- 4.1 General Architecture -- 4.2 Proposed Architecture for the Healthcare System -- 4.3 Proposed Model -- 4.4 Component Description -- 4.5 Working Procedure of Healthcare Model -- 5 The Need and Importance of Machine Learning in Smart Healthcare -- 6 Conclusion -- References -- Architecture for Smart Healthcare: Cloud Versus Edge -- 1 Introduction -- 2 Role of Cloud Computing in Smart Healthcare Architecture -- 2.1 Properties of Cloud Computing -- 2.2 Utilities of Cloud Computing -- 2.3 Deployment Models of Cloud Computing -- 2.4 Different Architectures of Cloud Computing Used in Smart Healthcare -- 3 Role of Edge Computing in Smart Healthcare Architecture -- 3.1 Utilities of Edge Computing -- 3.2 Different Proposed Architectures of Edge Computing Used in Smart Healthcare -- 3.3 Limitation of Edge Computing -- 4 Difference Between Cloud of Edge on Smart Healthcare Perspective -- 5 Conclusion -- References -- The Medical Internet of Things: A Review of Intelligent Machine Learning and Deep Learning Applications for Leveraging Healthcare -- 1 Introduction -- 2 The Architecture of MIoT -- 2.1 The Perception Layer -- 2.2 The Network Layer -- 2.3 The Application Layer -- 3 IoT in Healthcare -- 3.1 IoT Services and Applications -- 3.2 IoT Healthcare Applications -- 3.3 Benefits of IoT in HealthCare -- 4 Machine Learning and Deep Learning Applications for MIoT -- 4.1 Machine Learning and Deep Learning Applications -- 5 Related Work and Discussion -- 5.1 Summary -- 6 Challenges -- 7 Future Directions -- 8 Conclusion -- References. ; Main Challenges and Concerns of IoT Healthcare -- 1 Introduction -- 2 Applications of IoT in Healthcare -- 3 Benefits of IoT in Healthcare -- 4 Role of Cloud and Edge Computing in IoT Healthcare Services -- 4.1 Role of Cloud Computing in IoT Healthcare Services -- 4.2 Role of Edge Computing in IoT Healthcare -- 5 Challenges and Concerns of Healthcare IoT Ecosystem -- 5.1 Security and Privacy -- 5.2 Device Vulnerability -- 5.3 Interoperability -- 5.4 Connectivity -- 5.5 Volume of Data and Its Analytics -- 5.6 Energy Efficiency -- 5.7 Trust Issues and Government Policies -- 5.8 Other Challenges -- 6 Open Issues -- 7 Conclusion -- References -- Challenges of Handling Data in IoT-Enabled Healthcare -- 1 Introduction -- 2 IoT Healthcare Services and Applications -- 2.1 Services -- 2.2 IoT Applications -- 3 Challenges of Health Data -- 3.1 Inter-Domain Authentication and Interoperability -- 3.2 Security and Privacy -- 3.3 Device Communication -- 3.4 Management of Data -- 4 Uncertainties in Data -- 4.1 Types of Uncertainty in Healthcare -- 4.2 Sources of Uncertainty in IoT System -- 5 Statistical Methods -- 6 Case Studies -- 6.1 Accuracy-Related Error Prediction -- 6.2 Results for Precision Error Reduction -- 7 Virtual Sensor-Based Infrastructure -- 8 Conclusion -- References -- Context and Body Vitals Monitoring Systems -- Human Activity Recognition Systems Based on Sensor Data Using Machine Learning -- 1 Introduction -- 1.1 Classification of HAR According to Sensor Deployment -- 1.2 General Components of HAR System -- 1.3 Few Challenges of HAR in IoHT with Their Solution -- 2 Activity Recognition Method: A Machine Learning (ML) Approach -- 2.1 Why ML is to Be Used in HAR -- 2.2 Data Preprocessing: -- 2.3 Learning and Inference -- 2.4 Why to Prefer Modern ML Techniques and Deep Learning -- 3 Different State-Of-The-Art ML/DL Techniques. ; 4 Future Direction of Research -- References -- Human Activity Recognition Systems Based on Audio-Video Data Using Machine Learning and Deep Learning -- 1 Introduction -- 2 HAR -- 3 Sensory Data, Audio and Video Data for HAR -- 3.1 Sensory Data -- 3.2 Audio Data -- 3.3 Video Data -- 4 ML and DL in HAR -- 4.1 ML in HAR -- 4.2 Challenges of ML in HAR -- 4.3 DL in HAR -- 4.4 Challenges of DL in HAR -- 5 Implementation of HAR -- 5.1 Experimental Environment -- 6 Evaluation of HAR Systems -- 6.1 Evaluation Methodologies -- 6.2 Evaluation Metrics -- 7 Case Studies -- 8 Conclusion -- References -- On Body Vitals Monitoring for Disease Prediction: A Systematic Survey -- 1 Introduction to the Need for Generalized IOT Healthcare Paradigm -- 2 Role of Body Vital Monitoring System for Disease Prediction in IOT Healthcare -- 3 Valuable Vital Signs and Their Need to Be Monitored -- 3.1 Electrocardiogram (ECG) -- 3.2 Heart Rate (HR) -- 3.3 Blood Pressure (BP) -- 3.4 Respiration Rate -- 3.5 Blood Oxygen Saturation (SpO2) -- 3.6 Body Temperature -- 4 Review on the Recent Technological Advances in the Remote Healthcare Monitoring System -- 5 Summary -- References -- Review of Body Vitals Monitoring Systems for Disease Prediction -- 1 Introduction -- 2 Relevant Works -- 2.1 Data Collection -- 2.2 Analysis Techniques -- 3 Open Research Issues -- 4 Applications of Smart Devices -- 4.1 Personal ECG Monitor -- 4.2 Portable Smart Watch -- 4.3 Smart Glucometer -- 4.4 Brain Sensing Headband -- 4.5 Smart Temporal Thermometer -- 4.6 Fertility Tracking Bracelet -- 4.7 Pain Relief Device -- 4.8 Bio Scarf -- 5 Data Security -- 6 Technical Analysis -- 6.1 Data Preprocessing -- 6.2 Feature Extraction -- 6.3 Learning Approaches -- 6.4 Performance Measure -- 7 Conclusion -- References -- Quantitative Assessment of Smartphone Usage in College Students-A Digital Phenotyping Approach. ; 1 Introduction -- 2 Experiment and Study Design -- 2.1 Study Procedure -- 2.2 Incentives and Privacy Considerations -- 2.3 Data Collection -- 3 Smartphone Technology -- 3.1 Working of an Accelerometer -- 4 Activity Detection -- 5 Sleep Detection -- 6 Sociability -- 7 Location and Mobility Patterns -- 7.1 Mobility Patterns -- 8 Discussions -- 8.1 Smartphone Addiction: Mobile Usage Hours -- 8.2 Sleep Quality Impact -- 9 Conclusions -- References -- Home Automation System Combining Internet-of-Things with Brain-Computer Interfacing -- 1 Introduction -- 1.1 The Application of Smart Health Care -- 1.2 Home Automation Related to Health Care -- 1.3 Human Brain -- 1.4 Electrode Placement in Human Brain -- 1.5 Brain-Computer Interface -- 1.6 Electroencephalogram Using Machine Learning -- 1.7 Internet-of-Things Devices and Technologies -- 2 Literature Survey -- 3 Proposed Work -- 3.1 Electrode Placement and Data Acquisition -- 3.2 Preprocessing -- 3.3 Signal Processing -- 3.4 Application Device Control System -- 4 Results -- 4.1 Experimental Setup -- 4.2 EEG Recording and Data Collection -- 5 Conclusion and Future Direction -- References -- Social Sensing Applications for Public Health -- ``Montaj'': A Gaming System for Assessing Cognitive Skills in a Mobile Computing Platform -- 1 Introduction -- 2 Design and Development -- 2.1 Device Compatibility and System Installation -- 2.2 Life Cycle of a Study -- 2.3 Use Cases Diagram -- 2.4 Data Flow Diagram (DFD) -- 2.5 Entity Relationship (ER) Diagram -- 2.6 Games in `Montaj': Registration and Removal-Flexibility of the System -- 2.7 Games Made Available with the System -- 2.8 Built in Search Facilities of `Montaj' -- 2.9 Provision for Importing New Games -- 3 Testing and Validation -- 4 Novelty of System Implemented in the System -- 4.1 Capability of Accommodating Externally Implemented Games. ; 4.2 Novelty of the Games Implemented in the System -- 5 Usability Analysis -- 6 Conclusion -- References -- Social Data Analysis Techniques and Applications -- 1 Introduction -- 2 Social Sensing Application in Public Health -- 3 Social Data in Public Health -- 4 Social Data Analysis Techniques in Public health -- 4.1 Natural Language Processing -- 4.2 News Analytic -- 4.3 Opinion Mining -- 4.4 Scraping -- 4.5 Sentiment Analysis -- 4.6 Text Analytics -- 4.7 Social Network Analysis -- 5 State of Art Social Data Applications -- 6 SNA Techniques and Applications in Public Health -- 7 Conclusion -- References -- Challenges and Limitations of Social Data Analysis Approaches -- 1 Introduction -- 2 General Challenges -- 2.1 Technical Limitations -- 2.2 Actionability Concern -- 2.3 Ethical Considerations -- 3 Other Issues for Social Data Analysis -- 3.1 Issues of Social Data Collection -- 3.2 Issues for Processing Data -- 3.3 Issues Analysing Data -- 4 Social Data in Real-World Applications -- 5 Conclusion -- References -- Reliability, Security and Privacy of Health Data -- IoT-Based Secure Health Care: Challenges, Requirements and Case Study -- 1 Introduction -- 2 Traditional Versus Modern Architecture -- 3 Security Threats -- 4 Importance of Privacy and Security -- 4.1 Mutual-Authentication -- 4.2 Confidentiality -- 4.3 Anonymity -- 4.4 Data Freshness -- 4.5 Data Integrity -- 4.6 Data Availability -- 5 Taxonomy of Privacy and Security in Health Care -- 5.1 Access Control Schemes -- 5.2 Authentication Schemes -- 5.3 Encryption Schemes -- 6 Case Study on Intrusion Detection System for Health Care -- 6.1 Models -- 6.2 Desired Features of Healthcare IDS and IPS -- 6.3 Commercial IDS and IPS -- 6.4 Open Research Issues -- 7 Conclusion -- References. ; Applications of IoT and Blockchain Technologies in Healthcare: Detection of Cervical Cancer Using Machine Learning Approaches.
• Titolo autorizzato: Internet of things based smart healthcare
• ISBN: 981-19-1408-7
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910584483603321
• Lo trovi qui: Univ. Federico II
• Serie: Smart Computing and Intelligence