Wearable Computing

Giancarlo Fortino is a Professor of Computer Engineering at the Dept. of Informatics, Modeling, Electronics, and Systems of the University of Calabria (Unical), Italy. He received a Ph.D. in Computer Engineering from Unical, in 1995 and 2000, respectively. He is also adjunct professor at Wuhan University of Technology (Wuhan, China) and senior research fellow at the Italian National Research Council ICAR Institute. His research interests include agent-based computing, wireless (body) sensor networks, and Internet of Things. He is author of over 350 papers in int'l journals, conferences and books. He is cofounder and CEO of SenSysCal S.r.l., a Unical spinoff focused on innovative IoT systems. Fortino is currently member of the IEEE SMCS BoG and chair of the IEEE SMCS Italian Chapter. Raffaele Gravina is Assistant Professor of Computer Engineering at the Dept. of Informatics, Modeling, Electronics, and Systems of the University of Calabria (Unical), Italy. He received a Ph.D. in Computer and Systems Engineering from Unical, in 2012. His research interests include wireless body sensor networks, smart-Health, and IoT technology. He is author of over 60 papers in int'l journals, conferences and books. He is cofounder and CTO healthcare sector of SenSysCal S.r.l., a Unical spin-off focused on innovative IoT systems. Stefano Galzarano received the B.S., M.S., and PhD degrees in computer engineering from the University of Calabria, Rende, Italy, in 2006, 2009, and 2014, respectively. He is currently an external research collaborator of the University of Calabria. He authored several research papers in journals, conferences and books. His research interests are focused on high-level programming methods for wireless sensor networks and, specifically, novel methods and frameworks for autonomic wireless body sensor networks.

• Preface xiAcknowledgments xvi1 Body Sensor Networks 11.1 Introduction 11.2 Background 11.3 Typical m‐Health System Architecture 41.4 Hardware Architecture of a Sensor Node 61.5 Communication Medium 71.6 Power Consumption Considerations 71.7 Communication Standards 81.8 Network Topologies 101.9 Commercial Sensor Node Platforms 131.10 Biophysiological Signals and Sensors 161.11 BSN Application Domains 171.12 Summary 20References 202 BSN Programming Frameworks 252.1 Introduction 252.2 Developing BSN Applications 252.2.1 Application‐ and Platform‐Specific Programming 262.2.2 Automatic Code Generation 282.2.3 Middleware‐Based Programming 282.2.4 Programming Approaches Comparison 302.3 Programming Abstractions 312.4 Requirements for BSN Frameworks 342.5 BSN Programming Frameworks 372.5.1 Titan 382.5.2 CodeBlue 382.5.3 RehabSPOT 382.5.4 SPINE 392.5.5 SPINE2 392.5.6 C‐SPINE 392.5.7 MAPS 402.5.8 DexterNet 402.6 Summary 40References 413 Signal Processing In‐Node Environment 453.1 Introduction 453.2 Background 463.3 Motivations and Challenges 463.4 The SPINE Framework 463.4.1 Architecture 473.4.2 Programming Perspective 513.4.3 Optional SPINE Modules 513.4.4 High‐Level Data Processing 523.4.5 Multiplatform Support 553.5 Summary 56References 574 Task‐Oriented Programming in BSNs 594.1 Introduction 594.2 Background 604.3 Motivations and Challenges 604.3.1 Need for a Platform‐Independent Middleware 604.3.2 Challenges in Designing a Task‐Oriented Framework 614.4 SPINE2 Overview 624.5 Task‐Oriented Programming in SPINE2 634.6 SPINE2 Node‐Side Middleware 664.7 SPINE2 Coordinator 684.8 SPINE2 Communication Protocol 684.9 Developing Application in SPINE2 704.10 Summary 71References 725 Autonomic Body Sensor Networks 735.1 Introduction 735.2 Background 735.3 Motivations and Challenges 745.4 State‐of‐the‐Art 755.5 SPINE‐*: Task‐Based Autonomic Architecture 765.6 Autonomic Physical Activity Recognition 815.7 Summary 84References 856 Agent‐Oriented Body Sensor Networks 896.1 Introduction 896.2 Background 896.2.1 Agent‐Oriented Computing and Wireless Sensor Networks 896.2.2 Mobile Agent Platform for Sun SPOT (MAPS) 916.3 Motivations and Challenges 946.4 State‐of‐the‐Art: Description and Comparison 956.5 Agent‐Based Modeling and Implementation of BSNs 986.6 Engineering Agent‐Based BSN Applications: A Case Study 986.7 Summary 101References 1037 Collaborative Body Sensor Networks 1077.1 Introduction 1077.2 Background 1087.3 Motivations and Challenges 1097.4 State‐of‐the‐Art 1107.5 A Reference Architecture for Collaborative BSNs 1117.6 C‐SPINE: A CBSN Architecture 1147.6.1 Inter‐BSN Communication 1167.6.2 BSN Proximity Detection 1177.6.3 BSN Service Discovery 1187.6.4 BSN Service Selection and Activation 1187.7 Summary 119References 1198 Integration of Body Sensor Networks and Building Networks 1218.1 Introduction 1218.2 Background 1218.2.1 Building Sensor Networks and Systems 1218.2.2 Building Management Framework 1248.3 Motivations and Challenges 1258.4 Integration Layers 1278.5 State‐of‐the‐Art: Description and Comparison 1298.6 An Agent‐Oriented Integration Gateway 1308.7 Application Scenarios 1338.7.1 In‐Building Physical Activity Monitoring 1338.8 Summary 135References 1359 Integration of Wearable and Cloud Computing 1399.1 Introduction 1399.2 Background 1409.2.1 Cloud Computing 1409.2.2 Architectures for Sensor Stream Management 1409.3 Motivations and Challenges 1429.3.1 BSN Challenges 1439.3.2 BSN/Cloud Computing Integration Challenges 1449.4 Reference Architecture for Cloud‐Assisted BSNs 1459.4.1 Sensor Data Collection 1459.4.2 Sensor Data Management 1479.4.3 Scalable Processing Framework 1479.4.4 Persistent Storage 1489.4.5 Decision‐Making Process 1499.4.6 Open Standards and Advanced Visualization 1499.4.7 Security 1509.5 State‐of‐the‐Art: Description and Comparison 1519.5.1 Integration of WSNs and Cloud Computing 1519.5.2 Integration of BSNs and Cloud Computing 1529.5.3 A Comparison 1539.6 BodyCloud: A Cloud‐based Platform for Community BSN Applications 1569.7 Engineering BodyCloud Applications 1599.7.1 ECGaaS: Cardiac Monitoring 1609.7.2 FEARaaS: Basic Fear Detection 1629.7.3 REHABaaS: Remote Rehabilitation 1659.7.4 ACTIVITYaaS: Community Activity Monitoring 1669.8 Summary 171References 17110 Development Methodology for BSN Systems 17710.1 Introduction 17710.2 Background 17710.3 Motivations and Challenges 18010.4 SPINE‐Based Design Methodology 18010.4.1 A Pattern‐Driven Application‐Level Design 18110.4.2 System Parameters 18310.4.3 Process Schema 18410.5 Summary 186References 18611 SPINE‐Based Body Sensor Network Applications 18711.1 Introduction 18711.2 Background 18711.3 Physical Activity Recognition 18711.3.1 Related Work 18811.3.2 A SPINE‐Based Activity Recognition System 18911.4 Step Counter 19111.4.1 Related Work 19111.4.2 A SPINE‐Based Step Counter 19211.5 Emotion Recognition 19411.5.1 Stress Detection 19411.5.1.1 Related Work 19411.5.1.2 SPINE‐HRV: A Wearable System for Real‐Time Stress Detection 19511.5.2 Fear Detection 19711.5.2.1 Related Work 19711.5.2.2 A SPINE‐Based Startle Reflex Detection System 19811.6 Handshake Detection 20011.6.1 Related Work 20111.6.2 A SPINE‐Based Handshake Detection System 20211.7 Physical Rehabilitation 20511.7.1 Related Work 20511.7.2 SPINE Motor Rehabilitation Assistant 20611.8 Summary 208References 20812 SPINE at Work 21312.1 Introduction 21312.2 SPINE 1.x 21312.2.1 How to Install SPINE 1.x 21612.2.2 How to Use SPINE 21712.2.3 How to Run a Simple Desktop Application Using SPINE1.3 22012.2.4 SPINE Logging Capabilities 22512.3 SPINE2 22512.3.1 How to Install SPINE2 22812.3.2 How to Use the SPINE2 API 23012.3.3 How to Run a Simple Application Using SPINE2 232Index 239