5G Impact on Biomedical Engineering: Wireless Technologies Applications

209 Pages·2022·23.359 MB·English

by  Abdallah Makhoul, Jacques Demerjian, Jacques Bou Abdo

5G Impact on
Biomedical Engineering
Prospects in Biomedical Engineering and Applications
Series Editor:
Mohammad M. Banat, Jordan University of Science and Technology, Jordan
Adel Al-Jamaily, Faculty of Engineering and IT, University of Technology Sydney
5G Impact on Biomedical Engineering
Jacques Bou Abdo, Jacques Demerjian, Abdallah Makhoul
For more information about this series, please visit:
https://www.crcpress.com/Prospects-in-Biomedical-Engineering-and-
Applications/book-series/PBIOMED
5G Impact on
Biomedical Engineering
Wireless Technologies Applications
Edited by
Jacques Bou Abdo
Jacques Demerjian
Abdallah Makhoul
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ISBN: 978-0-367-52384-8 (hbk)
ISBN: 978-0-367-52802-7 (pbk)
ISBN: 978-1-003-05843-4 (ebk)
DOI: 10.1201/9781003058434
Typeset in CMR10
by KnowledgeWorks Global Ltd.
Contents
Preface xi
Editor Biographies xiii
List of Figures xv
List of Tables xvii
Contributors xix
Symbols xxi
I Introduction 1
1 Healthcare 4.0: Technologies and Policies 3
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Technology and e-Health . . . . . . . . . . . . . . . . . . . . 4
1.2.1 e-Health through Cloud Computing . . . . . . . . . . 4
1.2.2 e-Health through Internet of Things . . . . . . . . . . 5
1.2.3 e-Health through 5G . . . . . . . . . . . . . . . . . . . 7
1.3 Policy Challenges . . . . . . . . . . . . . . . . . . . . . . . . 8
1.3.1 Trust and Data Privacy . . . . . . . . . . . . . . . . . 9
1.3.2 Incentives for Using e-Health . . . . . . . . . . . . . . 11
1.3.3 Responsibility and Evidence . . . . . . . . . . . . . . . 11
1.3.4 Spectrum Licensing and Regulation . . . . . . . . . . 12
1.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2 Management of Collaborative BSN in Smart Environments 19
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2 BSN Architecture and Technologies . . . . . . . . . . . . . . 20
2.2.1 General Architecture . . . . . . . . . . . . . . . . . . . 20
2.2.2 BSN Applications . . . . . . . . . . . . . . . . . . . . 20
2.2.2.1 Medical Applications . . . . . . . . . . . . . 20
2.2.2.2 Non-Medical Applications . . . . . . . . . . . 22
2.2.3 Sensors Types, Properties, and Challenges . . . . . . . 22
2.2.3.1 Sensors Types . . . . . . . . . . . . . . . . . 22
2.2.3.2 BSN Challenges . . . . . . . . . . . . . . . . 24
v
vi Contents
2.2.4 Sensors’ Wireless Communication Technologies . . . . 24
2.3 From BSN to CBSN . . . . . . . . . . . . . . . . . . . . . . . 25
2.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . 25
2.3.2 CBSN Concept and Architecture . . . . . . . . . . . . 26
2.3.3 CBSN Applications. . . . . . . . . . . . . . . . . . . . 27
2.3.4 Comparison between BSN and CBSN . . . . . . . . . 28
2.3.5 Major Challenges in CBSN . . . . . . . . . . . . . . . 28
2.3.6 Open Research Issues in CBSN . . . . . . . . . . . . . 28
2.3.6.1 Sensor Nodes . . . . . . . . . . . . . . . . . . 30
2.3.6.2 Data Fusion . . . . . . . . . . . . . . . . . . 30
2.3.6.3 MAC Protocols . . . . . . . . . . . . . . . . 30
2.3.6.4 Routing . . . . . . . . . . . . . . . . . . . . . 31
2.3.6.5 Inter-BSN Communication . . . . . . . . . . 31
2.3.6.6 Coverage and Connectivity . . . . . . . . . . 31
2.3.6.7 Localization and Tracking. . . . . . . . . . . 32
2.3.6.8 Power Supply and Energy Concern. . . . . . 32
2.3.6.9 Security . . . . . . . . . . . . . . . . . . . . . 32
2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
II Communication Technologies 37
3 Smart Resource Allocation for LoRaWAN-based e-Health
Applications in Dense Deployments 39
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . 41
3.2.1 SF Allocation in LoRaWAN . . . . . . . . . . . . . . . 42
3.2.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . 43
3.3 System Model and Specifications . . . . . . . . . . . . . . . . 44
3.4 Optimization Problem for SF Selection . . . . . . . . . . . . 45
3.5 Spreading Factor Selection Game in LoRaWAN . . . . . . . 45
3.6 Distributed Learning for SF Selection in LoRaWAN . . . . . 48
3.7 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . 48
3.7.1 SF Selection Game vs. EXP3 . . . . . . . . . . . . . . 49
3.7.2 Energy Efficiency in LoRaWAN . . . . . . . . . . . . . 50
3.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
4 Dynamic Health Assessment in Water Environments using
LPWAN Technologies 57
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.2 Application Domains in Water Environments . . . . . . . . . 58
4.2.1 First Aid Operations . . . . . . . . . . . . . . . . . . . 59
4.2.2 Monitoring Floods . . . . . . . . . . . . . . . . . . . . 59
4.3 Real-time Monitoring Systems in Water Environments . . . . 60
4.3.1 Discovering Navigation Environment . . . . . . . . . . 60
Contents vii
4.3.2 SurvivorsIdentificationandAssessmentofTheirHealth
Conditions . . . . . . . . . . . . . . . . . . . . . . . . 61
4.4 Wireless Communication in Water Networks . . . . . . . . . 63
4.4.1 LTE-M Communication . . . . . . . . . . . . . . . . . 63
4.4.2 NB-IoT Communication . . . . . . . . . . . . . . . . . 64
4.4.3 LoRa Communication . . . . . . . . . . . . . . . . . . 64
4.5 Proposed LoRa-based Monitoring System . . . . . . . . . . . 65
4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
5 Quality of Service Provisioning for Ambulance Tele-medicine
in a Slice-based 5G Network 73
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
5.2 Tele-medicine 5G Network Slice . . . . . . . . . . . . . . . . 74
5.2.1 Network Slicing . . . . . . . . . . . . . . . . . . . . . . 74
5.2.2 5G Reference Slices . . . . . . . . . . . . . . . . . . . 75
5.2.3 Tele-Medicine Network Slice Architecture . . . . . . . 75
5.3 Mobility Management Solution Overview . . . . . . . . . . . 78
5.3.1 Slice Attachment . . . . . . . . . . . . . . . . . . . . . 79
5.3.2 Slice Handover Solution . . . . . . . . . . . . . . . . . 80
5.4 Slice Selection Function . . . . . . . . . . . . . . . . . . . . . 81
5.4.1 Related works. . . . . . . . . . . . . . . . . . . . . . . 81
5.4.2 Slice Selection Algorithm . . . . . . . . . . . . . . . . 82
5.4.3 End-to-End Slice Load Utility Calculation . . . . . . . 82
5.4.4 Candidates PoA QoS Utility Calculation . . . . . . . . 83
5.4.5 Target Slice Selection . . . . . . . . . . . . . . . . . . 84
5.5 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . 84
5.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
6 Routing Protocol Algorithms for Single-Body and
Multi-Body Sensor Networks 91
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
6.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . 93
6.3 Comparison of Different Routing Models . . . . . . . . . . . 95
6.4 An Efficient Cluster-based Routing Model . . . . . . . . . . 98
6.4.1 Cluster Formation . . . . . . . . . . . . . . . . . . . . 98
6.4.2 Cluster Head Election . . . . . . . . . . . . . . . . . . 99
6.4.3 Routing Operation . . . . . . . . . . . . . . . . . . . . 99
6.5 Implementation and Results . . . . . . . . . . . . . . . . . . 101
6.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
viii Contents
III Applications 111
7 Towards WBSNs Based Healthcare Applications:
From Energy-Efficient Data Collection to Fusion 113
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
7.2 WBSN: Architecture and Biosensor Nodes . . . . . . . . . . 114
7.3 Healthcare Applications . . . . . . . . . . . . . . . . . . . . . 115
7.4 Healthcare Application Requirements . . . . . . . . . . . . . 117
7.5 Energy-Efficient Mechanisms . . . . . . . . . . . . . . . . . . 118
7.6 Multi-sensor Data Fusion . . . . . . . . . . . . . . . . . . . . 120
7.7 Challenging Aspects in Data . . . . . . . . . . . . . . . . . . 122
7.8 High-Level Fusion: Data-Driven vs Knowledge-Driven
Approaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
7.9 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
7.10 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
8 Data Quality Management for Pervasive Health
Monitoring in Body Sensor Networks 129
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
8.2 Data Quality Basic Concepts . . . . . . . . . . . . . . . . . . 130
8.2.1 Data Quality Dimensions . . . . . . . . . . . . . . . . 132
8.2.2 Data Quality Factors. . . . . . . . . . . . . . . . . . . 134
8.2.2.1 Sensor level . . . . . . . . . . . . . . . . . . . 134
8.2.2.2 Human level . . . . . . . . . . . . . . . . . . 135
8.2.2.3 Network level . . . . . . . . . . . . . . . . . . 136
8.3 Data Quality Remedies . . . . . . . . . . . . . . . . . . . . . 136
8.3.1 Data Cleaning Approaches in WSNs . . . . . . . . . . 136
8.3.2 Data Cleaning Approaches in Healthcare Industry . . 138
8.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
9 Wireless Techniques and Applications of the Internet of
Medical Things 147
9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
9.2 Historical view and trends of IoMT in medical applications . 148
9.2.1 Physiological Analysis . . . . . . . . . . . . . . . . . . 148
9.2.2 Rehabilitation Systems. . . . . . . . . . . . . . . . . . 148
9.2.3 Nutritional Evaluation and Skin Pathologies. . . . . . 149
9.2.4 Epidemic Infections and Diseases Spot Localization. . 149
9.2.5 Diabetes Treatment . . . . . . . . . . . . . . . . . . . 149
9.3 Advantages of IoMT . . . . . . . . . . . . . . . . . . . . . . 149
9.4 Wireless Technology for Healthcare . . . . . . . . . . . . . . 150
9.5 Mobile Communications for Healthcare . . . . . . . . . . . . 152
9.5.1 Security Threats . . . . . . . . . . . . . . . . . . . . . 153
9.5.2 Wireless Communication and HIPAA Compliance . . 153
Contents ix
9.5.3 ConsiderationsofWirelessTechnologyintheHealthcare
System . . . . . . . . . . . . . . . . . . . . . . . . . . 153
9.6 IoT-based Healthcare Applications . . . . . . . . . . . . . . 154
9.6.1 IoMT-based Health Monitoring . . . . . . . . . . . . . 155
9.6.2 Application of COVID-19 Fighting Using Cognitive
Internet of Medical Things . . . . . . . . . . . . . . . 158
9.6.3 Early Identification and Monitoring of COVID-19
Individuals Deploying IoMT-based Framework . . . . 161
9.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
10 Deep Learning for IoT-Healthcare Based on
Physiological Signals 167
10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
10.2 Physiological Signals . . . . . . . . . . . . . . . . . . . . . . . 169
10.2.1 Electrocardiogram . . . . . . . . . . . . . . . . . . . . 169
10.2.2 Photoplethysmogram. . . . . . . . . . . . . . . . . . . 170
10.2.3 Electromyogram . . . . . . . . . . . . . . . . . . . . . 170
10.2.4 Electrodermal Activity . . . . . . . . . . . . . . . . . . 170
10.2.5 Electroencephalography . . . . . . . . . . . . . . . . . 170
10.3 Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . 171
10.3.1 Generative Models . . . . . . . . . . . . . . . . . . . . 173
10.3.1.1 Restricted Boltzmann Machine . . . . . . . . 173
10.3.1.2 Autoencoder . . . . . . . . . . . . . . . . . . 174
10.3.2 Hybrid Models . . . . . . . . . . . . . . . . . . . . . . 174
10.3.3 Discriminative Models . . . . . . . . . . . . . . . . . . 175
10.3.3.1 Multi-Layer Perceptron . . . . . . . . . . . . 175
10.3.3.2 Convolutional Neural Network . . . . . . . . 175
10.3.3.3 Long Short-Term Memory . . . . . . . . . . 175
10.4 Deep Learning-based Physiological Signals Analysis . . . . . 176
10.4.1 Time Series Classification . . . . . . . . . . . . . . . . 176
10.4.2 Physiological Signals Cleaning . . . . . . . . . . . . . 177
10.4.3 Artifacts Removal . . . . . . . . . . . . . . . . . . . . 179
10.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 180
Index 185