Contents

Cover

Title page

Copyright page

List of Figures

List of Tables

Foreword

Preface

Acknowledgments

Acronyms

Part I: Virtual Reality, Augmented Reality Technologies and Applications For Health and Medicine

Chapter 1: Reviews of The Implications of VR/AR Health Care Applications in Terms of Organizational and Societal Change
1. 1.1 Introduction
2. 1.2 Virtual Reality and Augmented Reality
3. 1.3 Features of VR/AR Technology in Health Care
4. 1.4 Future Assessments in VR/AR Technology
5. 1.5 Key Challenges for Adopting VR/AR Technology
6. 1.6 Conclusion
7. References
Chapter 2: Using 3D Simulation in Medical Education: A Comparative Test of Teaching Anatomy Using Virtual Reality
1. 2.1 Introduction
2. 2.2 Literature Review of Training with Medical VR
3. 2.3 Methodology of this Study
4. 2.4 Results
5. 2.5 Discussion
6. References
Chapter 3: Building Empathy in Young Children Using Augmented Reality: A Case Study in Malaysia
1. 3.1 Introduction
2. 3.2 Motivations
3. 3.3 Literature Review
4. 3.4 Proposed Approach
5. 3.5 Results and Discussions
6. 3.6 Conclusions
7. References
Chapter 4: Effectiveness of Virtual Reality Mock Interview Training
1. 4.1 Introduction
2. 4.2 Virtual Reality Training Literature Review
3. 4.3 Methodology
4. 4.4 Results
5. 4.5 Disscussion
6. 4.6 Conclusions
7. References
Chapter 5: Augmenting Dental Care: A Current Perspective
1. 5.1 Introduction
2. 5.2 Augmented Reality Technology in Medical Technology
3. 5.3 Existing Technologies in Medical / Healthcare Technology
4. 5.4 Augmenting Dental Care- AR Technologies assisting Dentists for Dental Care
5. 5.5 Augmented Reality in Dental Education
6. 5.6 Augmented Reality based Education Technologies for Dentistry
7. 5.7 Conclusion
8. References
Chapter 6: Review of Virtual Reality Evaluation Methods and Psychophysiological Measurement Tools
1. 6.1 Science Can Help Inform Virtual Reality Development
2. 6.2 Virtual Reality Can Help Inform Psychology and Science
3. 6.3 Types of Psychophysiological Measures and Tools
4. 6.4 Outcome of the Evaluation
5. 6.5 Conclusions
6. References

Part II: Artificial Intelligence Technologies and Applications for Health and Medicine

Chapter 7: State of The Art: Artificial Intelligent Technologies for Mobile Health of Stroke Monitoring and Rehabilitation Robotics Control
1. 7.1 Introduction
2. 7.2 Research Chapter Objectives
3. 7.3 Literature Review
4. 7.4 Description of the Research Telemedicine Platform
5. 7.5 A proposed intelligent adaptive behavior control to rehabilitation robotics
6. 7.6 Materials and Methods
7. 7.7 Conclusion Summary: Artificial Intelligence Technologies
8. References
Chapter 8: Artificial Intelligence for Smart Cancer Diagnosis: A New Telemedicine System Based on Fuzzy Image Segmentation
1. 8.1 Introduction
2. 8.2 Background and Related work
3. 8.3 Proposed System Architecture
4. 8.4 Telemedicine System Modules
5. 8.5 Results and discussion
6. 8.6 Conclusion and Future Work
7. References
Chapter 9: Mobile Doctor Brain AI App: Artificial Intelligence for Iot Healthcare
1. 9.1 Introduction
2. 9.2 State of the Art
3. 9.3 Proposed System Design
4. 9.4 Proposed Artificial Intelligence Techniques for New AI loT Health-Care Solutions for Stroke Monitoring
5. 9.5 Conclusion
6. References
Chapter 10: An Artificial Intelligence Mobile-Cloud Computing Tool: Experimental Results Based on Physicians’ and Patients’ Views of Cancer Care by Family Medicine
1. 10.1 Introduction
2. 10.2 Background and State-of-the-Art
3. 10.3 Development and Proposing a New Intelligent case-based Reasoning Decision Engine for Cacer Diagnosis
4. 10.4 Experimental Results of The Proposed System
5. 10.5 Conclusion
6. References
Chapter 11: Advanced Intelligent Robot Control Interfaces for The Virtual Reality Simulation Applied to the Induction Hardening Process
1. 11.1 Introduction
2. 11.2 Proposed Mechanical Structure
3. 11.3 Unit 3D Integration
4. 11.4 Results
5. 11.5 Conclusion
6. Acknowledgments
7. References
Chapter 12: Analysis of Telemedicine Technologies
1. 12.1 Introduction
2. 12.2 Literature Review
3. 12.3 Architecture of Telemedicine Technologies
4. 12.4 Enabling Technologies for Telemedicine
5. 12.5 Conclusion
6. References

Part III: Robotics Technologies and Applications for Health and Medicine

Chapter 13: Critical Position using Environment Model Applied on Walking Robots
1. 13.1 Introduction
2. 13.2 On the Environment’s Mathematical Model
3. 13.3 Physical and Mathematical Models of The Walking Robot Leg
4. 13.4 On Critical Positions of 3D Walking Robots
5. 13.5 Mathematical model of beam without damping
6. 13.6 Mathematical Model of Beam with Viscous Damping
7. 13.7 Conclusion
8. References
Chapter 14: The Walking Robot Equilibrium Recovery Applied on The NAO Robot
1. 14.1 Introduction
2. 14.2 The Choice of the Model
3. 14.3 Mathematical Modeling of Two-Link Biped Walking Robot
4. 14.4 Linear Control Design
5. 14.5 Results and Discussion
6. 14.6 Conclusions
7. References
Chapter 15: Development of A Robotic Teaching Aid for Disabled Children in Malaysia
1. 15.1 Introduction
2. 15.2 Case Study - Autism
3. 15.3 Movitations
4. 15.4 Proposed Approach
5. 15.5 Results and Discussions
6. 15.6 Robotic Intervention Enhance Autistic Students’ Engagement, Interaction and Focus
7. 15.7 Conclusion
8. References
Chapter 16: Training System Design of Lower Limb Rehabilitation Robot Based on Virtual Reality
1. 16.1 Introduction
2. 16.2 Application Device
3. 16.3 Trajectory Planning and Smooth Motion
4. 16.4 Virtual Reality Training System
5. 16.5 Virtual Reality Software Design 16.5.1 Virtual Scene Build
6. 16.6 Virtual Reality Training Experiment
7. 16.7 Conclusion
8. Acknowledgements
9. References

Part IV: Internet of Things Technologies and Applications for Health and Medicine

Chapter 17: Automation of Appliances Using Electroencephalography
1. 17.1 Introduction
2. 17.2 Background, History and Future Aspects
3. 17.3 Brain with Its Main Parts and Their Functions
4. 17.4 Working of BCI
5. 17.5 BCI Classes
6. 17.6 Conclusion
7. References
Chapter 18: Designing A Beautiful Life for Indian Blind Peoples: A Smart Stick
1. 18.1 Introduction
2. 18.2 Internet of Things
3. 18.3 Background
4. 18.4 Purpose Approach
5. 18.5 Implementation
6. 18.6 Advantages and Disadvantages
7. 18.7 Conclusion
8. References
Chapter 19: Smart Home: Personal Assistant and Baby Monitoring System
1. 19.1 Introduction
2. 19.2 Background
3. 19.3 Proposed Design and Implementation
4. 19.4 Online Energy Meter
5. 19.5 Sensors used and Their Working
6. 19.6 Conclusion
7. References

End User License Agreement

List of Illustrations

Chapter 1

Figure 1.1 (a) Example of Virtual Reality [10], (b) Example of Augmented Reality Training in Health care [4]
Figure 1.2 Relationship of Real and Virtual Environment (Augmented Reality or Mixed Reality) [15]
Figure 1.3 Levels of VR Immersion (a) A Non Immersive VR System (b) A Semi Immersive VR System (c) A Fully Immersive VR System [10]
Figure 1.4 AR Systems Formats (a) Marked AR System. (b) Mark less AR System [38]
Figure 1.5 Gadgets and Wearable Devices used in Health Care Applications [37]

Chapter 2

Figure 2.1 3D virtual reality simulation of human anatomy
Figure 2.2 Practicing in Virtual Reality
Figure 2.3 Design of the Study
Figure 2.4 Three teaching methods: A. Plastic models, B. Real cadaver, C. Virtual Reality
Figure 2.5 The scores of the different university students (HPMU, DTU and BMTU) after the first post-training exam
Figure 2.6 The scores of the different university students (HPMU, DTU and BMTU) after the second post-training exam
Figure 2.7 The scores of the different university students (HPMU, DTU and BMTU) grouped together per condition (Manikin, Cadaver, VR) after the first post-training exam (Post test 1, yellow), and the second post-training exam (Post test 2, red)
Figure 2.8 The aggregated scores of all university students (HPMU, DTU and BMTU) grouped together per condition (Manikin, Cadaver, VR), with the first post-training exam scores (Post test 1, orange), and the scores on the second post-training exam (Post test 2, green)

Chapter 3

Figure 3.1 AR Market Predictions
Figure 3.2 An empathy scene
Figure 3.3 The response buttons
Figure 3.4 Among the Participants
Figure 3.5 Testing on Preschool Students

Chapter 4

Figure 4.1 Oculus Rift Consumer Version 1
Figure 4.2 Example of two users communicating in a Virtual Reality space
Figure 4.3 Image of Virtual Reality Interview Training Session
Figure 4.4 Participants response to measure 12
Figure 4.5 Participants responses to measure 13

Chapter 5

Figure 5.1 Oral and Maxillofacial Surgery- Before and After Results
Figure 5.2 3D Patient Skull Generation
Figure 5.3 Dental Implant Surgery via Screw fitted on Jawbone
Figure 5.4 Orthognathic Surgery
Figure 5.5 Dental Simulation
Figure 5.6 DentSim Real Time Root Canal Treatment Simulation

Chapter 6

Figure 6.1 The iterative process of VR Development
Figure 6.2 Usability and other potential acceptance criteria, Nielsen’s Usability Diagram.
Figure 6.3 The process of empirical evaluation
Figure 6.4 Electrode Placement to recording Galvanic Skin Response
Figure 6.5 Electrode Placement to recording facial expressions with EMG
Figure 6.6 The development cycle, using Rapid prototype <> test cycles by the Interaction Design Foundation

Chapter 7

Figure 7.1 Intelligent Telemedicine Rehabilitation Robotic Architecture
Figure 7.2 Hierarchical Intelligent Behavior Control for Robot
Figure 7.3 Intelligent Behavior Control Algorithm
Figure 7.4 General process model for Telemedicine sensor data management
Figure 7.5 Mobile Patient Emergency for Stroke Patients to Nearest Hospital
Figure 7.6 Artificial Intelligence Technologies Components

Chapter 8

Figure 8.1 Basic Service-Oriented Architecture
Figure 8.2 SOA Service Communication using WCF
Figure 8.3 SOA implemented as WCF process and services
Figure 8.4 The Proposed Telemedicine System Modules
Figure 8.5 Block Diagram of Image Compression Using Wavelet Technique
Figure 8.6 Two level wavelet decomposition
Figure 8.7 Image Enhancement and ROI segmentation flowchart
Figure 8.8 Results of Image Enhancement and Region of Interest Segmentation
Figure 8.9 Mammogram images while applying steps of Fuzzy C-Mean algorithm steps. (a) Original image, (b) image with segmented ROI after applying the morphological operators, (c) The resulted image after clustering

Chapter 9

Figure 9.1 Mobile Doctor Brain AI App
Figure 9.2 Research Area 1: AI for Raspberry pi - system on chip
Figure 9.3 Research Area 2: AI Real-time EMG Human Motion Analysis
Figure 9.4 General process model for Artificial Intelligence Telemedicine sensor data management (Three Layers: Signal Processing, Mobile Data Aggregation with AI Engine an Cloud CBR Patients Expert system)
Figure 9.5 Patient Emergency Scenario for Stroke/Heart Attack Elderly and Expert Doctor Recommendations
Figure 9.6 Architecture of support vector machine
Figure 9.7 Classification accuracies for RBF kernel with different sigma values for Kneeing and Pulling actions
Figure 9.8 Case-Based Reasoning Cycle
Figure 9.9 EMG Commerical Shimmer Sensor

Chapter 10

Figure 10.1 A hierarchical Case-Based Decision Support System for Cancer Diagnosis on three levels of medical expert
Figure 10.2 Frame scheme of the 44 real medical features of thyroid cancer

Chapter 11

Figure 11.1 Proposed mechanical structure of Ro CIF VIP with 5 DOF
Figure 11.2 Translation Joint Class diagram
Figure 11.3 Top (a) and bottom (b) sliding prismatic joint
Figure 11.4 Top (a) and bottom (b) gripper prismatic joint
Figure 11.5 Extraction prismatic joint
Figure 11.6 Root component which starts the entire simulation
Figure 11.7 SimStarter class diagram
Figure 11.8 GUI Class which provides commands to the user
Figure 11.9 GUI required for controlling the virtual simulation
Figure 11.10 GUI used to compute CIF parameters using intelligent interfaces
Figure 11.11 GUI Class for navigating through the virtual environment
Figure 11.12 Pan (a), Zoom (b), and Rotation (c) navigation with mouse
Figure 11.13 SimManager class diagram required for accessing the simulated components from within the app
Figure 11.14 Slider class diagram inherited by each Translation Joint class
Figure 11.15 Ro CIF VIP class component required to link the simulated objects with the manager variables
Figure 11.16 Save to XML experimental data, class diagram
Figure 11.17 Saved data for Top Sliding (a) and Bottom Sliding (b) prismatic joint
Figure 11.18 Saved data for Top Gripper (a) and Bottom Gripper (b) prismatic joint
Figure 11.19 Saved data for Extractor prismatic joint

Chapter 12

Figure 12.1 Telemedicine Technologies
Figure 12.2 Telemedicine Architecture

Chapter 13

Figure 13.1 Physical model of the walking robot leg; Bt and OE are center of circle respectively ellipse arc trajectories; P1, P_A, P, P_B are knee joint positions in leg cyclic evolution; Q₁, Q_A, Q, Q_B are base point positions in leg cyclic evolution; P^*_A, P^*_B; Q^*_A; Q^*_B are critical positions
Figure 13.2 Physical model of three dimensional walking robot leg

Chapter 14

Figure 14.1 Two-link inverted pendulum model in the sagittal plane
Figure 14.2 Stabilization is done in 18 seconds, with a disturbance to the ankle and the hip of xo = [0.02, 0.03, 0, 0] and a low R value
Figure 14.3 Stabilization is done in 35 seconds, with a disturbance to the ankle and the hip of xo = [0.02, 0.03, 0, 0] and a high R value
Figure 14.4 Stabilization is done in 18 seconds, with a disturbance to the ankle and the hip of x₀ = [–0.02, 0.03, 0, 0] and a low R value
Figure 14.5 Stabilization is done in 60 seconds, with a disturbance to the ankle and the hip of x₀ = [–0.02, 0.03, 0, 0] and a high R value
Figure 14.6 Results for disturbance only to the hip x₀ = [0, 0.03, 0, 0] with high R values, stabilization is done in 40 seconds.
Figure 14.7 Results for disturbance only to the hip x₀ = [0, 0.03, 0, 0] with lower R values, stabilization is done in 20 seconds.

Chapter 15

Figure 15.1 The shapes and colors
Figure 15.2 The fixed motor directions
Figure 15.3 RoboTherapist flowchart
Figure 15.4 RoboTherapist
Figure 15.5 Survey on students’ attentiveness
Figure 15.6 Survey on the effectiveness of robotic approach
Figure 15.7 Opinions on robotic approach
Figure 15.8 Traditional method in teaching basic shapes using cardboard and whiteboard
Figure 15.9 After 10 minutes learning autistic children started to lose their interest
Figure 15.10 Autistic children still attracted to learn even after 20 minutes
Figure 15.11 Hands-on learning
Figure 15.12 Test after learning process with Robot

Chapter 16

Figure 16.1 Lower limb rehabilitation robot
Figure 16.2 Left mechanical leg
Figure 16.3 Necessary sensor element
Figure 16.4 Linkage model of LLRR mechanical leg
Figure 16.5 Comparison between the original path and the new path planned
Figure 16.6 The angular position of the end point at X axis and Y axis
Figure 16.7 The velocity in the direction of X axis and Y axis
Figure 16.8 The acceleration in the direction of X axis and Y axis
Figure 16.9 The angular position of three joints
Figure 16.10 The angular velocity of three joints
Figure 16.11 The acceleration of three joints
Figure 16.12 The angular position curves at X axis and Y axis
Figure 16.13 Angular velocity curves at the line, X axis and Y axis
Figure 16.14 The acceleration in the direction of the line, X axis and Y axis
Figure 16.15 The displacement of three joints
Figure 16.16 The velocity of three joints
Figure 16.17 The acceleration of three joints The general equation of dynamics was obtained,
Figure 16.18 Riding body posture
Figure 16.19 Calculated circular trajectory
Figure 16.20 Interaction control strategy
Figure 16.21 Match scene in game
Figure 16.22 First-person perspective
Figure 16.23 Function test
Figure 16.24 Screenshot of synchronization test
Figure 16.25 Feedback terrains test

Chapter 17

Figure 17.1 Brain Anatomy
Figure 17.2 Nervous System
Figure 17.3 Neuron
Figure 17.4 Pyramidal Neuron
Figure 17.5 Pyramidal Neuron Chain
Figure 17.6 Synapse with Pyramidal Neuron
Figure 17.7 Neurotransmitters
Figure 17.8 Neuron Dipole
Figure 17.9 Working of BCI
Figure 17.10 Delta Waves
Figure 17.11 Theta Waves
Figure 17.12 Alpha Waves
Figure 17.13 Beta Waves
Figure 17.14 Gamma Waves
Figure 17.15 Steps
Figure 17.16 Electrode position
Figure 17.17 Electrode Cap side view
Figure 17.18 Differential Amplifier
Figure 17.19 Differential Amplifier Working
Figure 17.20 Fp2-F8 Single Tracing
Figure 17.21 Chain
Figure 17.22 Bipolar Montage Electrodes
Figure 17.23 Anterior-Posterior Montage
Figure 17.24 Eye Deflection Readings
Figure 17.25 BCI methods Implantation

Chapter 18

Figure 18.1 Ultrasonic Sensor Working
Figure 18.2 NodeMCU Board [4]
Figure 18.3 NodeMCU pin diagram [5]
Figure 18.4 Simple Buzzer
Figure 18.5 Basic Flow Diagram
Figure 18.6 7805 IC
Figure 18.7 Breadboard
Figure 18.8 SNAP Connector
Figure 18.9 Female to Female wire, Male to Male wire, Male to Female wire
Figure 18.10 All Components
Figure 18.11 Circuit Diagram
Figure 18.12 Connection of 7805 IC and Battery with Bread board
Figure 18.13 NodeMCU connections
Figure 18.14 Buzzer Implementation

Chapter 19

Figure 19.1 Overview of system
Figure 19.2 Connection of Arduino with ACS712 sensor and Relay
Figure 19.3 Raspberry Pi and connected different modules
Figure 19.4 Baby Monitoring System and connected sub modules
Figure 19.5 Graph of Body Temperature of a baby
Figure 19.6 Graph of Pulse-Rate of a baby
Figure 19.7 Steps of face detection and Recognition
Figure 19.8 Energy Measurement and Conservation Module
Figure 19.9 LM35 Temperature Sensor
Figure 19.10 IR Temperature Sensor
Figure 19.11 Soil Moisture Sensor
Figure 19.12 PIR Motion Sensor
Figure 19.13 Sound Sensor
Figure 19.14 Pulse Rate Sensor
Figure 19.15 Accelerometer ADXL335 Module
Figure 19.16 Accelerometer sensor MEM mechanism
Figure 19.17 Sensor with neutral position
Figure 19.18 The sensor in a flexed position
Figure 19.19 ACS712 Current Sensor
Figure 19.20 Graph 1
Figure 19.21 Graph 2
Figure 19.22 Graph 3
Figure 19.23 Flow of process for Online Energy Meter module
Figure 19.24 Real Power
Figure 19.25 Reactive Power
Figure 19.26 Apparent Power
Figure 19.27 Power Factor
Figure 19.28 Root Mean Square

List of Illustrations

Chapter 1

Table 1.1 Standard terms in Virtual Reality and Augmented Reality
Table 1.2 Design Elements in Virtual Reality and Augmented Reality
Table 1.3 Smart phones health care apps

Chapter 2

Table 2.1 Age and gender variation among groups and conditions (Group A plastic manikin, group B real cadaver, group C Virtual Reality)
Table 2.2 The statistical summary of pre-test and post-test 1 scores
Table 2.3 The statistics of scoring average after swapping participants from Manikin condition to VR condition

Chapter 3

Table 3.1 Comparative studies on the existing AR applications for empathy development [6]
Table 3.2 Overall Result of EMPARTI Evaluation Form

Chapter 4

Table 4.1 SPSS output for Paired Samples T-Test comparing participants anxiety levels prior-to and post-to the VR mock interview

Chapter 5

Table 5.1 The foundation highlighting the Origin of Augmented Reality

Chapter 6

Table 6.1 Overview of Design and Evaluation methods and recommended time and setting for using them

Chapter 9

Table 9.1 Sample of published experimental results

Chapter 10

Table 10.1 Retrieval Accuracy of the CBIR

Chapter 12

Table 12.1 Multiple Barrier on different themes

Chapter 14

Table 14.1 Parameters of the NAO robot

Chapter 15

Table 15.1 Participants Details
Table 15.2 Test Assessment Results (traditional vs robotic intervention)

Chapter 19

Table 19.1 Few libraries used in this system
Table 19.2 Minimum and maximum value obtained from soil moisture sensor
Table 19.3 Respiration rate as per the age group

Virtual Reality, Augmented Reality, Artificial Intelligence, Internet of Things, Robotics, Industry 4.0

Dac-Nhuong Le

Deputy-Head, Faculty of Information Technology, Haiphong University, Haiphong, Vietnam

Chung Van Le

CVS Center, Duy Tan University, Danang, Vietnam

Jolanda G. Tromp

University of New York in Oswego, NY, USA

Gia Nhu Nguyen

Dean, Graduate School, Duy Tan University, Danang, Vietnam

List of Figures

1.1 (a) Example of Virtual Reality [10], (b) Example of Augmented Reality Training in Health care [4]

1.2 Relationship of Real and Virtual Environment (Augmented Reality or Mixed Reality) [15]

1.3 Levels of VR Immersion (a) A Non Immersive VR System (b) A Semi Immersive VR System (c) A Fully Immersive VR System [10]

1.4 AR Systems Formats (a) Marked AR System. (b) Mark less AR System [38]

1.5 Gadgets and Wearable Devices used in Health Care Applications [37]

2.1 3D virtual reality simulation of human anatomy

2.2 Practicing in Virtual Reality

2.3 Design of the Study

2.4 Three teaching methods: A. Plastic models, B. Real cadaver, C. Virtual Reality

2.5 The scores of the different university students (HPMU, DTU and BMTU) after the first posttraining exam

2.6 The scores of the different university students (HPMU, DTU and BMTU) after the second posttraining exam

2.7 The scores of the different university students (HPMU, DTU and BMTU) grouped together per condition (Manikin, Cadaver, VR) after the first posttraining exam (Post test 1, yellow), and the second posttraining exam (Post test 2, red)

2.8 The aggregated scores of all university students (HPMU, DTU and BMTU) grouped together per condition (Manikin, Cadaver, VR), with the first posttraining exam scores (Post test 1, orange), and the scores on the second posttraining exam (Post test 2, green)

3.1 AR Market Predictions

3.2 An empathy scene

3.3 The response buttons

3.4 Among the Participants

3.5 Testing on Preschool Students

4.1 Oculus Rift Consumer Version 1

4.2 Example of two users communicating in a Virtual Reality space

4.3 Image of Virtual Reality Interview Training Session

4.4 Participants response to measure 12

4.5 Participants responses to measure 13

5.1 Oral and Maxillofacial Surgery-Before and After Results

5.2 3D Patient Skull Generation

5.3 Dental Implant Surgery via Screw fitted on Jawbone

5.4 Orthognathic Surgery

5.5 Dental Simulation

5.6 DentSim Real Time Root Canal Treatment Simulation

6.1 The iterative process of VR Development

6.2 Usability and other potential acceptance criteria, Nielsen’s Usability Diagram.

6.3 The process of empirical evaluation

6.4 Electrode Placement to recording Galvanic Skin Response

6.5 Electrode Placement to recording facial expressions with EMG

6.6 The development cycle, using Rapid prototype <> test cycles by the Interaction Design Foundation

7.1 Intelligent Telemedicine Rehabilitation Robotic Architecture

7.2 Hierarchical Intelligent Behavior Control for Robot

7.3 Intelligent Behavior Control Algorithm

7.4 General process model for Telemedicine sensor data management

7.5 Mobile Patient Emergency for Stroke Patients to Nearest Hospital

7.6 Artificial Intelligence Technologies Components

8.1 Basic Service-Oriented Architecture

8.2 SOA Service Communication using WCF

8.3 SOA implemented as WCF process and services

8.4 The Proposed Telemedicine System Modules

8.5 Block Diagram of Image Compression Using Wavelet Technique

8.6 Two level wavelet decomposition

8.7 Image Enhancement and ROI segmentation flowchart

8.8 Results of Image Enhancement and Region of Interest Segmentation

8.9 Mammogram images while applying steps of Fuzzy C-Mean algorithm steps. (a) Original image, (b) image with segmented ROI after applying the morphological operators, (c) The resulted image after clustering

9.1 Mobile Doctor Brain AI App

9.2 Research Area 1: AI for Raspberry pi - system on chip

9.3 Research Area 2: AI Real-time EMG Human Motion Analysis

9.4 General process model for Artificial Intelligence Telemedicine sensor data management (Three Layers: Signal Processing, Mobile Data Aggregation with AI Engine an Cloud CBR Patients Expert system)

9.5 Patient Emergency Scenario for Stroke/Heart Attack Elderly and Expert Doctor Recommendations

9.6 Architecture of support vector machine

9.7 Classification accuracies for RBF kernel with different sigma values for Kneeing and Pulling actions

9.8 Case-Based Reasoning Cycle

9.9 EMG Commerical Shimmer Sensor

10.1 A hierarchical Case-Based Decision Support System for Cancer Diagnosis on three levels of medical expert

10.2 Frame scheme of the 44 real medical features of thyroid cancer

11.1 Proposed mechanical structure of Ro CIF VIP with 5 DOF

11.2 Translation Joint Class diagram

11.3 Top (a) and bottom (b) sliding prismatic joint

11.4 Top (a) and bottom (b) gripper prismatic joint

11.5 Extraction prismatic joint

11.6 Root component which starts the entire simulation

11.7 SimStarter class diagram

11.8 GUI Class which provides commands to the user

11.9 GUI required for controlling the virtual simulation

11.10 GUI used to compute CIF parameters using intelligent interfaces

11.11 GUI Class for navigating through the virtual environment

11.12 Pan (a), Zoom (b), and Rotation (c) navigation with mouse

11.13 SimManager class diagram required for accessing the simulated components from within the app

11.14 Slider class diagram inherited by each Translation Joint class

11.15 Ro CIF VIP class component required to link the simulated objects with the manager variables

11.16 Save to XML experimental data, class diagram

11.17 Saved data for Top Sliding (a) and Bottom Sliding (b) prismatic joint

11.18 Saved data for Top Gripper (a) and Bottom Gripper (b) prismatic joint

11.19 Saved data for Extractor prismatic joint

12.1 Telemedicine Technologies

12.2 Telemedicine Architecture

13.1 Physical model of the walking robot leg; Bt and OE are center of circle respectively ellipse arc trajectories; P₁; P_A; P; P_B are knee joint positions in leg cyclic evolution; Q₁;Q_A; Q;Q_B are base point positions in leg cyclic evolution; P*_A;P*_B;Q*_A;Q*_B are critical positions

13.2 Physical model of three dimensional walking robot leg

14.1 Twolink inverted pendulum model in the sagittal plane

14.2 Stabilization is done in 18 seconds, with a disturbance to the ankle and the hip of x₀ = [0:02; 0:03; 0; 0] and a low R value

14.3 Stabilization is done in 35 seconds, with a disturbance to the ankle and the hip of x₀ = [0:02; 0:03; 0; 0] and a high R value

14.4 Stabilization is done in 18 seconds, with a disturbance to the ankle and the hip of x₀ = [–0:02; 0:03; 0; 0] and a low R value

14.5 Stabilization is done in 60 seconds, with a disturbance to the ankle and the hip of x₀ = [–0:02; 0:03; 0; 0] and a high R value

14.6 Results for disturbance only to the hip x₀ = [0; 0:03; 0; 0] with high R values, stabilization is done in 40 seconds.

14.7 Results for disturbance only to the hip x₀ = [0; 0:03; 0; 0] with lower R values, stabilization is done in 20 seconds.

15.1 The shapes and colors

15.2 The fixed motor directions

15.3 RoboTherapist flowchart

15.4 RoboTherapist

15.5 Survey on students’ attentiveness

15.6 Survey on the effectiveness of robotic approach

15.7 Opinions on robotic approach

15.8 Traditional method in teaching basic shapes using cardboard and whiteboard

15.9 After 10 minutes learning autistic children started to lose their interest

15.10 Autistic children still attracted to learn even after 20 minutes

15.11 Hands-on learning

15.12 Test after learning process with Robot

16.1 Lower limb rehabilitation robot

16.2 Left mechanical leg

16.3 Necessary sensor element

16.4 Linkage model of LLRR mechanical leg

16.5 Comparison between the original path and the new path planned

16.6 The angular position of the end point at X axis and Y axis

16.7 The velocity in the direction of X axis and Y axis

16.8 The acceleration in the direction of X axis and Y axis

16.9 The angular position of three joints

16.10 The angular velocity of three joints

16.11 The acceleration of three joints

16.12 The angular position curves at X axis and Y axis

16.13 Angular velocity curves at the line, X axis and Y axis

16.14 The acceleration in the direction of the line, X axis and Y axis

16.15 The displacement of three joints

16.16 The velocity of three joints

16.17 The acceleration of three joints

16.18 Riding body posture

16.19 Calculated circular trajectory

16.20 Interaction control strategy

16.21 Match scene in game

16.22 First-person perspective

16.23 Function test

16.24 Screenshot of synchronization test

16.25 Feedback terrains test

17.1 Brain Anatomy

17.2 Nervous System

17.3 Neuron

17.4 Pyramidal Neuron

17.5 Pyramidal Neuron Chain

17.6 Synapse with Pyramidal Neuron

17.7 Neurotransmitters

17.8 Neuron Dipole

17.9 Working of BCI

17.10 Delta Waves

17.11 Theta Waves

17.12 Alpha Waves

17.13 Beta Waves

17.14 Gamma Waves

17.15 Steps

17.16 Electrode position

17.17 Electrode Cap side view

17.18 Differential Amplifier

17.19 Differential Amplifier Working

17.20 Fp2-F8 Single Tracing

17.21 Chain

17.22 Bipolar Montage Electrodes

17.23 Anterior-Posterior Montage

17.24 Eye Deflection Readings

17.25 BCI methods Implantation

18.1 Ultrasonic Sensor Working

18.2 NodeMCU Board [4]

18.3 NodeMCU pin diagram [5]

18.4 Simple Buzzer

18.5 Basic Flow Diagram

18.6 7805 IC

18.7 Breadboard

18.8 SNAP Connector

18.9 Female to Female wire, Male to Male wire, Male to Female wire

18.10 All Components

18.11 Circuit Diagram

18.12 Connection of 7805 IC and Battery with Bread board

18.13 NodeMCU connections

18.14 Buzzer Implementation

19.1 Overview of system

19.2 Connection of Arduino with ACS712 sensor and Relay

19.3 Raspberry Pi and connected different modules

19.4 Baby Monitoring System and connected sub modules

19.5 Graph of Body Temperature of a baby

19.6 Graph of Pulse-Rate of a baby

19.7 Steps of face detection and Recognition

19.8 Energy Measurement and Conservation Module

19.9 LM35 Temperature Sensor

19.10 IR Temperature Sensor

19.11 Soil Moisture Sensor

19.12 PIR Motion Sensor

19.13 Sound Sensor

19.14 Pulse Rate Sensor

19.15 Accelerometer ADXL335 Module

19.16 Accelerometer sensor MEM mechanism

19.17 Sensor with neutral position

19.18 The sensor in a flexed position

19.19 ACS712 Current Sensor

19.20 Graph 1

19.21 Graph 2

19.22 Graph 3

19.23 Flow of process for Online Energy Meter module

19.24 Real Power

19.25 Reactive Power

19.26 Apparent Power

19.27 Power Factor

19.28 Root Mean Square

Foreword

There are some key factors driving the increasing adoption of augmented reality (AR) and virtual reality (VR) technologies, which depend mainly on the growing integration of technology and digitization in the field of healthcare, as well as increasing healthcare expenditures which focus on delivery of efficient health services and its significance in training healthcare professionals. The advanced technologies related to AR and VR have a great effect on the healthcare industry with their adoption in virtual training of surgeons in 3D operating room simulations for difficult surgeries and as phobia buster in mental health treatment as well as for chronic pain management. Also, VR plays a major role in eye movement desensitization and reprocessing (EMDR) therapy to enable reframing of traumatic memories through certain eye movements. Furthermore, this technology offers benefits in various areas of care management such as autism and depression therapy, cancer therapy, and assisted living. VR-based organ models have played a crucial part in preparing surgeons for delicate and complicated operations that demand greater precision, less complications, and reduced trauma. On the other hand, AR is considered a useful active and powerful tool for training and education. AR-based applications are effectively used to provide the improved care of many patients. For example, the vein visualization technology, developed by AccuVein Inc. was developed to handle scanning, which helps doctors and nurses successfully locate veins and valves at the first go, reducing pain and the required time. These applications are also used in the aftercare of patients and assist elderly people in managing their medications. This book focuses on adopting robots in conjunction with VR and AR to help in healthcare and medicine applications; for instance, we discuss a training system developed for a lower limb rehabilitation robot based on virtual reality (VR), mainly including trajectory planning and VR control strategy. It can simulate bike riding and encourages patients to join in their recovery and rehabilitation through a built-in competitive game. The robot could achieve linear trajectory, circle trajectory and arbitrary trajectory based on speed control, in which the training velocity and acceleration in the trajectory planning have been simulated. A human-machine dynamics equation was built which is used to judge the intent of a patient’s movement. The VR training mode is a variable speed active training under the constraint trajectory, and it has an adapting training posture function which can provide an individual riding training track according to the leg length of patients. The movement synchronization between the robot and virtual model was achieved by interaction control strategy, and the robot can change the training velocity based on the signal from feedback terrains in the game. A serious game about a bike match in a forest was designed in which the user can select the training level as well as change perspective through the user interface.

The main purpose of this book is to publish the best papers submitted to the special session on VR/AR Healthcare and Medicine Applications at the International Conference on Communication, Management and Information Technology (ICCMIT 2018) in Madrid, Spain.¹ ICCMIT 2018 is an annual meeting for scientists, engineers and academicians to discuss the latest discoveries and realizations in the foundations, theory, models and applications of nature-inspired systems, and emerging areas related to the three tracks of the conference covering all aspects of communication, engineering, management, and information technology given by panels made up of world-class speakers and at workshops.

Prof. Ibrahiem El Emary
Prof. Musbah J. Aqel
International Cyprus University