The book covers the integration of Internet of Things (IoT) and Artificial Intelligence (AI) to tackle applications in smart healthcare. The authors discuss efficient means to collect, monitor, control, optimize, model, and predict healthcare data using AI and IoT. The book presents the many advantages and improvements in the smart healthcare field, in which ubiquitous computing and traditional computational methods alone are often inadequate. AI techniques are presented that play a crucial role in dealing with large amounts of heterogeneous, multi-scale and multi-modal data coming from IoT infrastructures. The book is intended to cover how the fusion of IoT and AI allows the design of models, methodologies, algorithms, evaluation benchmarks, and tools can address challenging problems related to health informatics, healthcare, and wellbeing.
Author(s): Carmela Comito, Agostino Forestiero, Ester Zumpano
Series: Internet of Things
Publisher: Springer
Year: 2022
Language: English
Pages: 187
City: Cham
Preface
References
Contents
Authors and Contributors
About the Authors
Contributors
Lower-Gait Tracking Application Using Smartphones and Tablets
1 Introduction
2 Materials and Methods
2.1 Mobility Analysis Workflow
2.2 Mobile Application and Devices
2.3 Vicon Motion Capture System
3 Results and Discussion
3.1 Main Features of the LGait Mobile App
3.2 Measurements Verification
3.3 Discussion
4 Conclusion
References
One-Class Classification Approach in Accelerometer-Based Remote Monitoring of Physical Activities for Healthcare Applications
1 Use Cases of HAR in Healthcare
2 System Architecture of HAR with IoT Integration
3 Components of HAR System
3.1 Data Acquisition with Sensors
3.2 Data Communication
3.3 Knowledge Discovery
3.3.1 Feature Extraction
3.3.2 Classification
3.3.3 Segmentation
4 One-Class Classification (OCC)
4.1 How to Generate Negative Class Data
5 IoT Testbed for OCC-Based Recognition
6 Dataset
7 Experimental Setup and Results
8 Chapter Summary
References
Detecting and Monitoring Behavioural Patterns in Individuals with Cognitive Disorders in the Home Environment with Partial Annotations
1 Introduction
2 The CUBOId Project
3 Visualising Sensor Data
4 Methods
4.1 Indoor Localisation
4.2 Machine Learning Models for Indoor Localisation
4.3 Model Selection with Weak Labels
5 Findings
5.1 Sleep Disturbance
5.2 Wandering
5.3 Shadowing
5.4 Identifying Change and Confounders
6 Conclusions and Future Work
References
Toward On-Device Weight Monitoring from Selfie Face Images Using Smartphones
1 Introduction
1.1 Our Contribution
2 Prior Work
3 Inference on the Server or on the Device
4 Convolutional Neural Networks Used
5 Experimental Validations
5.1 Dataset and Protocol
5.2 Results
6 Conclusion and Future Work
References
Convergence Between IoT and AI for Smart Health and Predictive Medicine
1 Introduction
2 Exploring IoT Technologies for Smart Healthcare
3 Exploring AI Techniques for Smart Healthcare
4 Opportunity and Obstacles of IoT and AI Integration
4.1 Benefits
4.2 Challenges
5 Conclusion
References
An Artificial Intelligence and Internet of Things Platform for Healthcare and Industrial Applications
1 Introduction
2 A Large-Scale AI and IoT Platform
2.1 AI Platform for Face Recognition
2.1.1 Introduction to Face Recognition
2.1.2 System Function Stacks
2.1.3 AI Pipeline
2.1.4 AI Engine: Feature Vector Extractor
2.1.5 Search Engine
2.2 IoT Platform
2.2.1 The IoT Pipeline
2.2.2 IoT Device Protocols
2.2.3 Web/Mobile App API Management
3 Application of the AI Platform in Tracing COVID-19 Patients and Close Contacts
3.1 Motivation
3.2 Fever Screening Camera
3.3 Tracing Fever Patients and Close Contacts
3.4 Demonstration of the User Console
4 Industrial Applications of the IoT Platform
5 Potential AI+IoT Application in Healthcare
5.1 Summary of Platform Features
5.2 Application in COVID-19 Diagnosis
5.3 Integration to Platform
6 Conclusions
References
Methods in Digital Mental Health: Smartphone-Based Assessment and Intervention for Stress, Anxiety, and Depression
1 Introduction: Short Morphogenesis of Assessment and Intervention
2 Digital Metamorphosis of Assessment and Intervention
2.1 Related Work
2.2 Digital Assessment
2.2.1 Stress
2.2.2 Anxiety
2.2.3 Depression
2.3 Digital Intervention
2.3.1 Stress
2.3.2 Anxiety and Depression
3 Conclusion: Speculative Evolution of Assessment and Intervention
References
AI for the Detection of the Diabetic Retinopathy
1 Introduction
2 Diabetic Retinopathy
3 The Contribution of AI for the Detection of the Diabetic Retinopathy
4 Concluding Remarks
References
Enhancing EEG-Based Emotion Recognition with Fast Online Instance Transfer
1 Introduction
2 Related Work
3 Methods
3.1 Conceptual Ideas for the Fast Online Transfer
3.2 Fast Online Instance Transfer
3.2.1 Model Weighting
3.2.2 Instance Selection
3.2.3 Model Ensemble
3.3 Active Learning
3.4 Transfer Component Analysis
3.5 Multi-Source Style Transfer Mapping
4 Experiments and Results
4.1 Dataset
4.2 Preprocessing
4.3 Implementation Details
4.4 Comparison with Baselines
4.5 Ablation Experiments
4.6 Comparison with Other Methods
4.7 Additional Evaluations
5 Discussions
6 Conclusion
References
Using Association Rules to Mine Actionable Knowledge from Internet of Medical Thinks Data
1 Introduction
2 Background
2.1 Internet of Medical Thinks
2.2 Association Rules
3 Proposed Approach
4 Conclusion
References
Index
