Enabling Person-Centric Healthcare Using Ambient Assistive Technology: Personalized and Patient-Centric Healthcare Services in AAT (Studies in Computational Intelligence, 1108)

Preface
Contents
About the Editors
Sensor Datasets for Human Daily Safety and Well-Being
	1 Introduction
	2 Air Quality Sensors
		2.1 Case Study: Maintenance of a Food Diary
	3 Camera RGB/RGB-D and Video Mocap Sensors
		3.1 Case Study: Robotic Coach for Active Ageing
	4 Conclusion and Open Challenges
	References
Habitpad: A Habit-Change Person-Centric Healthcare Mobile Application with Machine Leaning and Gamification Features for Obesity
	1 Introduction
	2 Related Work
		2.1 Habit Change
		2.2 Gamification
		2.3 Machine Learning Classification Algorithms
		2.4 Ambient Assisted Living Technology and Remote Person-Centric Healthcare
		2.5 Comparison of Existing Habit Change Healthcare Applications
	3 Development Methodology
		3.1 Planning and Requirement Analysis Phase
		3.2 Designing Phase
		3.3 Development Phase
		3.4 Testing and Integrating Phase
	4 Data Analytics Results
		4.1 Datasets
		4.2 Classification
		4.3 User Interface
	5 Deployment Strategy
	6 Conclusion and Future Work
	References
Human Centered Mathematics: A Framework for Medical Applications Based on Extended Reality and Artificial Intelligence
	1 Introduction
	2 XR and AI in Medical Applications
		2.1 XR in Medical Applications
		2.2 AI in Medical Applications
	3 A Framework for Medical Applications Based on XR and AI
	4 Results
	5 Future Directions in Human Centered Mathematics
	6 Conclusion
	References
Attentive Vision-Based Model for Sarcopenia Screening by Automating Timed Up-and-Go (TUG) Test
	1 Introduction
	2 Literature Review
	3 Methodology
		3.1 Implementation Environment
		3.2 Data Collection Procedure
	4 Results and Analysis
	5 Discussion
	6 Conclusion
	References
AAL with Deep Learning to Classify the Diseases Remotely from the Image Data
	1 Introduction
		1.1 Motivation of Scheming Ambient Assisted Living
	2 Implementing AAL Technology
	3 Deep Learning for Disease Classification
		3.1 Architectures of Deep Learning
	4 Proposed Work
		4.1 Layers of Deep Learning Architectures
	5 Incorporating Deep Learning in AAL Technology
		5.1 Alex Net Algorithm
		5.2 VGGNet 16 (2014)
		5.3 VGG Net 19
		5.4 ResNet
		5.5 DenseNet
		5.6 MobileNet
	6 Conclusion
	References
Heart Failure Prediction Using Radial Basis with Metaheuristic Optimization
	1 Introduction
	2 Role of Ambien-Assistive Living in Heart Failure Prediction
	3 Dataset
	4 Correlation Heatmap
	5 Proposed Methodology
		5.1 Pre Processing
		5.2 RBF
		5.3 RBF with Metaheuristic Optimization
	6 Experimental Setup and Results
	7 Conclusion
	References
Healthcare Management and Prediction of Future Illness Through Autonomous Intelligent Advisory System Using AAT Computational Framework
	1 Introduction
		1.1 The Role of Information Technology Services in Healthcare
		1.2 Ambient Assistive Technologies
		1.3 Ambient Assistive Living
		1.4 Ambient Assistive Technologies for Healthcare
		1.5 Benefits of AAL Technology Usage
		1.6 Challenges at the User and Caretaker Level
		1.7 Motivation
		1.8 Contribution of the Current Study
		1.9 Organization of the Chapter
	2 AAT Common Services, Tools/ Devices in Healthcare
		2.1 Categorization of Services in Assistive Living
		2.2 AAT Tools/devices
		2.3 Disrupting Technologies in AAL
	3 Self-Advocacy or Recommendation Systems in Healthcare
		3.1 Apps in Health Monitoring
		3.2 Apps for Smart Care
		3.3 Apps for Telehealth
		3.4 Apps for Smart Drugs
		3.5 Apps for Activity Tracking / Activities of Daily Living:
		3.6 Apps for Communication and Socialization:
	4 Autonomous Intelligent Advisory System Using Siot Computational Framework
	5 Sample Application in Health Scenario
	6 Conclusions
	References
ResNet-50-CNN and LSTM Based Arrhythmia Detection Model Based on ECG Dataset
	1 Introduction
	2 The Proposed Methodology
	3 The Overall Process of the Proposed System
		3.1 Data Acquisition
		3.2 Data Preprocessing and Visualization
		3.3 ECG Classification
	4 Results
	5 Discussions
	6 Conclusion
	References
A Review of Brain-Computer Interface (BCI) System: Advancement and Applications
	1 Introduction
	2 Application of BCIs
		2.1 Potential Medical Uses
		2.2 Non-biomedical Uses
	3 Principles of the BCI’s Operation
		3.1 Invasive
		3.2 Partially-Invasive
		3.3 Electrocorticography (ECoG)
		3.4 Non-invasive
	4 Signal Preprocessing and Signal Enhancement
		4.1 Independent Component Analysis
		4.2 Common Average Reference
		4.3 Adaptive Filters
		4.4 Principal Component Analysis
		4.5 Surface Laplacian (SL)
		4.6 Denoising Signal
	5 Feature Extraction Techniques
		5.1 Extracting Features from EEG Data
	6 Classification Techniques
		6.1 Linear Classifiers
		6.2 Neural Networks
	7 Literature Cited
	8 Challenges
		8.1 Feature Extraction
		8.2 Classification
		8.3 BCI Functionality and Hardware
		8.4 Information Gathering Mode
	9 Conclusion
	References
Optimized TSA ResNet Architecture with TSH—Discriminatory Features for Kidney Stone Classification from QUS Images
	1 Introduction
	2 Literature Survey
	3 Importance of Deep Learning in Medical Image Classification
	4 Proposed TSH-TSA-ResNet Architecture
		4.1 Data Collection of Kidney Stone Classification from QUS Images
	5 Experimental Results
		5.1 Dataset—Image Acquisition
		5.2 Implementation Platform
		5.3 Performance Analysis
	6 Discussion
	7 Conclusion
	References
Ambient Healthcare: A New Paradigm in Medical Zone
	1 Introduction
	2 Ambient Healthcare
		2.1 Ambient Intelligence Based Health Care Challenges
		2.2 Use Cases Driven by Ambient Intelligence
	3 Motivation and Purpose
	4 Analysis of Relevant Models on Ambient Healthcare
		4.1 BANs: Body Area Networks
		4.2 Dense/Mesh Sensor Networks for Smart Living Environments
	5 Applications of Ambient Intelligence in Healthcare
		5.1 Continuous Health Monitoring
		5.2 Continuous Behavioral Monitoring
		5.3 Assisted Living
		5.4 Emotional Well-Being
		5.5 Smart Hospitals
		5.6 Reducing ICU Stay
		5.7 Monitoring for Emergency Detection
		5.8 Therapy and Rehabilitation
	6 Case Study of Monitoring for Emergency Detection
	7 Future Challenges of Ambient Healthcare
		7.1 Artificial Intelligence
		7.2 Design and Human Factors
		7.3 Security and Infrastructure
		7.4 Socio-Ethical Issues
	8 Conclusion
	References
Illuminating Unexplored Corners in Healthcare Space Using Ambience Intelligence
	1 Introduction
	2 Related Works and Background Study
	3 Disease Management with Ambient Intelligence
	4 Critical Stakeholders in Healthcare Space
	5 Functional Requirements in Clinical Domain
	6 Disease Regulation and Healthcare Scenario
	7 Ambient Intelligence and Its Usage in Daily Life
	8 Social and Ethical Issues
	9 Technical Concerns and Methods to Overcome
	10 Limitations of Using Ambient Intelligence in Healthcare
	11 Conclusion
	References
Depression Assessment in Youths Using an Enhanced Deep Learning Approach
	1 Introduction
	2 Related Work
	3 Proposed System for Depression Assessment
	4 Results and Analysis
	5 Conclusion and Future Work
	References
Telemedicine Enabled Remote Digital Healthcare System
	1 Introduction
		1.1 Main Highlights of the Work is as Follows
	2 Background and Related Work
	3 Telemedical Ecosystem Model and Interfaces
	4 Implementation of the Telemedical Ecosystem Model
	5 Discussion
	6 Superiority of Telemedical-Ecosystem Model
	7 Future Scope
		7.1 Telemedicine Will Develop into a Common Service
		7.2 Will Develop Medical Services for Connected Health in Remote Areas
		7.3 Remote Patient Care Will Be a Reality
		7.4 Telemonitoring
		7.5 Teleconsultation
	8 Main Obstacles Faced in Telemedicine
		8.1 Misdiagnoses
		8.2 Compensation and Parity
		8.3 Cybersecurity
		8.4 Privacy Rule and Telemedicine
	9 Specific Challenges of Telemedicine
		9.1 Rural Areas
		9.2 On-Demand Nursing
		9.3 Patient Satisfaction
	10 Current Telemedicine Situation
		10.1 Government Cooperation
		10.2 Viable Software
		10.3 Software Legal Compliance
		10.4 Staff Training
		10.5 Identifying Sectors
	11 Conclusion
	References