Advances in Assistive Technologies: Selected Papers in Honour of Professor Nikolaos G. Bourbakis – Vol. 3 (Learning and Analytics in Intelligent Systems, 28)

This book aims at exposing its readers to some of the most significant advances in assistive technologies. Assistive technologies develop devices to assist vulnerable people, including elderly people, people with cognitive disabilities such as memory problems or dementia, and people with functional disabilities such as those needing support in toileting, moving around, eating, bathing, dressing, grooming, and taking personal care. The ever-increasing life expectancy and world population, along with the emergence of new diseases and the regrettable continuation of armed conflicts, press for development and availability of assistive technologies that offer people in need the opportunity to live secure and controlled lives with more active participation in society and at a reduced assistive cost. This task can be accomplished only with more advanced devices than traditional-powered wheelchairs or eating/drinking devices, including prosthetics, exoskeletons, visual and hearing aids, cognitive aids, and devices enhanced with artificial intelligence. The book consists of an editorial note and an additional twelve (12) chapters, all of which have been written by authors who work on the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into four parts, namely (i) Advances in Assistive Technologies in Healthcare, (ii) Advances in Assistive Technologies in Medical Diagnosis, (iii) Advances in Assistive Technologies in Mobility and Navigation, and (iv) Advances in Privacy and Explainability in Assistive Technologies. This research book is directed towards professors, researchers, scientists, engineers, and students in assistive technologies. It is also directed towards readers who come from other disciplines and is interested in becoming versed in some of the most recent assistive technologies. An extensive list of bibliographic references at the end of each chapter guides its readers to probe further into the application areas of interest to them. Foreword Preface Contents 1 Introduction to Advances in Assistive Technologies 1.1 Editorial Note 1.2 Book Summary and Future Volumes References Part I Advances in Assistive Technologies in Healthcare 2 Applications of AI in Healthcare and Assistive Technologies 2.1 Introduction 2.2 Healthcare and Biomedical Research 2.2.1 Controlled Monitoring Environment 2.2.2 Evolving Healthcare Techniques 2.2.3 Diagnosis 2.3 Assistive Technologies 2.3.1 Smart Homes and Cities 2.3.2 Assistive Robotics 2.4 Analysis and Forecasting 2.5 Conclusions References 3 A Research Agenda for Dementia Care: Prevention, Risk Mitigation and Personalized Interventions 3.1 Introduction 3.2 Mild Behavioral Impairments (MBI) and Dementia 3.3 Biometric Data 3.4 Caring for Caregivers 3.5 Tests 3.6 Conclusions References 4 Machine Learning and Finite Element Methods in Modeling of COVID-19 Spread 4.1 Introduction 4.1.1 Physiology of Human Respiratory System 4.1.2 Spreading of SARS-CoV-2 Virus Infection 4.1.3 Machine Learning for SARS-CoV-2 4.2 Methods 4.2.1 Finite Element Method for Airways and Lobes 4.2.2 Machine Learning Method 4.3 Results 4.3.1 Simulation of Virus Spreading by Finite Element Analysis 4.3.2 Machine Learning Results 4.4 Conclusions References Part II Advances in Assistive Technologies in Medical Diagnosis 5 Towards Personalized Nutrition Applications with Nutritional Biomarkers and Machine Learning 5.1 Introduction 5.1.1 Summary 5.1.2 Chapter Synopsis 5.1.3 Goals and Perspective 5.2 Basic Concepts 5.2.1 Personalized Medicine 5.2.2 Next Generation Sequencing 5.2.3 Obesity 5.2.4 Nutritional Biomarkers 5.3 Neural Networks, Pattern Recognition and Datasets 5.3.1 Neural Network 5.3.2 Implementation Environment 5.3.3 Proposed System 5.4 Implementation and Evaluation of the Proposed System 5.4.1 Deep Back Propagation Neural Network 5.4.2 Standard Biochemistry Profile Neural Network (SBPNN) 5.4.3 Neural Network Dietary Profile 5.5 Conclusions and Future Research 5.5.1 Prevention 5.5.2 Modelling 5.5.3 Automation 5.5.4 Perspective 5.5.5 Discussion on Feature Research 5.6 Appendix References 6 Inductive Machine Learning and Feature Selection for Knowledge Extraction from Medical Data: Detection of Breast Lesions in MRI 6.1 Introduction 6.2 Detailed Literature Review 6.3 Presentation of the Data 6.3.1 Data Collection 6.3.2 Description of Variables 6.3.3 Dataset Preprocessing 6.4 Methodology 6.4.1 Modeling Methodology 6.4.2 Feature Selection Process 6.4.3 Classification Method 6.4.4 Validation Process 6.5 Modeling Approaches 6.5.1 Experimental Process 6.5.2 Experimental Results 6.6 Conclusions and Further Search Annex 6.1—Abbreviations Annex 6.2—Variables Frequency Charts (Original Dataset) Annex 6.3—Variables’ Values Range Annex 6.4—Classification Tree (Benign or Malignant) References 7 Learning Paradigms for Neural Networks for Automated Medical Diagnosis 7.1 Introduction 7.2 Classical Artificial Neural Networks 7.3 Learning Paradigms 7.3.1 Evolutionary Computation Learning Paradigm 7.3.2 Bayesian Learning Paradigm 7.3.3 Markovian Stimulus-Sampling Learning Paradigm 7.3.4 Logistic Regression Paradigm 7.3.5 Ant Colony Optimization Learning Paradigm 7.4 Conclusions and Future Outlook References Part III Advances in Assistive Technologies in Mobility and Navigation 8 Smart Shoes for Assisting People: A Short Survey 8.1 Smart Shoes for People in Need 8.1.1 Smart Shoes for Visually Impaired People [6] 8.1.2 Smart Shoes for Blind Individuals [13] 8.1.3 IoT Based Wireless Smart Shoes and Energy Harvesting System [7] 8.1.4 Smart Shoes for Sensing Force [8] 8.1.5 Smart Shoes for Temperature and Pressure [9] 8.1.6 Smart Shoes in IoT [10] 8.1.7 Smart Shoes for People with Walking Disorders [23] 8.2 Special Purpose Smart Shoes 8.2.1 Smart Shoes with Triboelectric Nanogenerator [11] 8.2.2 Smart Shoes Gait Analysis [12] 8.2.3 Smart Shoes for Biomechanical Energy Harvesting [14] 8.2.4 Smart Shoes with Embedded Piezoelectric Energy Harvesting [15] 8.2.5 Pedestrian Navigation Using Smart Shoes with Markers [16] 8.2.6 Smart Shoes with 3D Tracking Capabilities [17] 8.2.7 Pedestrian’s Safety with Smart Shoes Sensing [18] 8.2.8 Smart Shoes Insole Tech for Injury Prevention [19] 8.3 Maturity Evaluation of the Smart Shoes 8.4 Conclusion References 9 Re-Examining the Optimal Routing Problem from the Perspective of Mobility Impaired Individuals 9.1 Introduction 9.2 Literature Review 9.2.1 Mobility Aspects for People with Special Needs 9.3 Related Work 9.3.1 Miller–Tucker–Zemlin Formulation of the Traveling Salesman Problem 9.3.2 Dantzig–Fulkerson–Johnson Formulation of the Traveling Salesman Problem 9.4 The Optimal Routing Problem from the Perspective of Mobility Impaired Individuals 9.4.1 Measuring Route Scores Based on the Degree of Accessibility 9.4.2 Problem Statement: The Optimal Routing Problem from the Perspective of Mobility Impaired Individuals 9.4.3 The Proposed Solution Approach 9.5 The Experimental Results and Discussion 9.6 Conclusions References 10 Human Fall Detection in Depth-Videos Using Temporal Templates and Convolutional Neural Networks 10.1 Introduction 10.2 Proposed Method 10.3 Experiments, Results and Discussion 10.3.1 SDU Fall Dataset 10.3.2 UP-Fall Detection Dataset 10.3.3 UR Fall Detection Dataset 10.3.4 MIVIA Action Dataset 10.4 Conclusions and Future Work 10.5 Compliance with Ethical Standards References 11 Challenges in Assistive Living Based on Tech Synergies: The Cooperation of a Wheelchair and A Wearable Device 11.1 Overall Description of the Challenges 11.2 Background and Significance 11.3 The Associated Research Challenges 11.3.1 Main Innovative Tasks 11.4 Discussion References 12 Human–Machine Requirements’ Convergence for the Design of Assistive Navigation Software: Τhe Case of Blind or Visually Impaired People 12.1 Introduction 12.2 Related Work 12.3 Methodology 12.3.1 Interviews with BVI People and Requirements Classification 12.3.2 Description of the Participants 12.3.3 Requirements Classification 12.4 Analysis of the Elicited Requirements 12.4.1 Elicited Requirements of the BVI 12.5 Discussion 12.6 Conclusion Appendix A References Part IV Advances in Privacy and Explainability in Assistive Technologies 13 Privacy-Preserving Mechanisms with Explainability in Assistive AI Technologies 13.1 Introduction 13.1.1 Data Ethics 13.1.2 Data Privacy 13.1.3 Data Security 13.2 AI Applications in Assistive Technologies 13.2.1 Explainable AI (XAI) 13.3 Data Privacy and Ethical Challenges for Assistive Technologies 13.3.1 Data Collection and Data Sharing 13.3.2 Secure and Responsible Data Sharing Framework 13.4 AI Assistive Technologies with Privacy Enhancing 13.4.1 Privacy-Preserving Mechanisms for AI Assistive Technologies 13.5 Discussions References