book

Wearable Computing

by Giancarlo Fortino, Raffaele Gravina, Stefano Galzarano

May 2018

Intermediate to advanced

256 pages

7h 7m

English

Wiley-IEEE Press

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1.1 Introduction1.2 Background1.3 Typical m‐Health System Architecture1.4 Hardware Architecture of a Sensor Node1.5 Communication Medium1.6 Power Consumption Considerations1.7 Communication Standards1.8 Network Topologies1.9 Commercial Sensor Node Platforms1.10 Biophysiological Signals and Sensors1.11 BSN Application Domains1.12 SummaryReferences
2.1 Introduction2.2 Developing BSN Applications2.3 Programming Abstractions2.4 Requirements for BSN Frameworks2.5 BSN Programming Frameworks2.6 SummaryReferences
3.1 Introduction3.2 Background3.3 Motivations and Challenges3.4 The SPINE Framework3.5 SummaryReferences
4.1 Introduction4.2 Background4.3 Motivations and Challenges4.4 SPINE2 Overview4.5 Task‐Oriented Programming in SPINE24.6 SPINE2 Node‐Side Middleware4.7 SPINE2 Coordinator4.8 SPINE2 Communication Protocol4.9 Developing Application in SPINE24.10 SummaryReferences
5.1 Introduction5.2 Background5.3 Motivations and Challenges5.4 State‐of‐the‐Art5.5 SPINE‐*: Task‐Based Autonomic Architecture5.6 Autonomic Physical Activity Recognition5.7 SummaryReferences
6.1 Introduction6.2 Background6.3 Motivations and Challenges6.4 State‐of‐the‐Art: Description and Comparison6.5 Agent‐Based Modeling and Implementation of BSNs6.6 Engineering Agent‐Based BSN Applications: A Case Study6.7 SummaryReferences

7.1 Introduction7.2 Background7.3 Motivations and Challenges7.4 State‐of‐the‐Art7.5 A Reference Architecture for Collaborative BSNs7.6 C‐SPINE: A CBSN Architecture7.7 SummaryReferences
8.1 Introduction8.2 Background8.3 Motivations and Challenges8.4 Integration Layers8.5 State‐of‐the‐Art: Description and Comparison8.6 An Agent‐Oriented Integration Gateway8.7 Application Scenarios8.8 SummaryReferences
9.1 Introduction9.2 Background9.3 Motivations and Challenges9.4 Reference Architecture for Cloud‐Assisted BSNs9.5 State‐of‐the‐Art: Description and Comparison9.6 BodyCloud: A Cloud‐based Platform for Community BSN Applications9.7 Engineering BodyCloud Applications9.8 SummaryReferences
10.1 Introduction10.2 Background10.3 Motivations and Challenges10.4 SPINE‐Based Design Methodology10.5 SummaryReferences
11.1 Introduction11.2 Background11.3 Physical Activity Recognition11.4 Step Counter11.5 Emotion Recognition11.6 Handshake Detection11.7 Physical Rehabilitation11.8 SummaryReferences
12.1 Introduction12.2 SPINE 1.x12.3 SPINE2

Content preview from Wearable Computing

3Signal Processing In‐Node Environment

3.1 Introduction

The analysis of the state‐of‐the‐art on the BSN domain has highlighted that the development of BSN applications is to date a complex task also due to the lack of programming frameworks with dedicated support to the distinctive requirements of BSN systems.

To support the programming of optimized BSN applications while minimizing the development time and effort, we have designed and realized SPINE (Signal Processing In‐Node Environment) [1–3], an open‐source domain‐specific programming framework for BSNs.

SPINE aims at boosting the prototyping of BSN applications. SPINE enables efficient implementations of signal‐processing algorithms for analysis and classification of sensor data through libraries of processing functionalities. It is organized into two interacting macro‐components, which are, respectively, implemented on commercially available programmable sensor devices and on the personal coordinator (Android smartphones and tablets, or a personal computer). Communication among these devices is wireless, using Bluetooth or IEEE 802.15.4 standards. The high‐level SPINE API (at the coordinator level) allows for dynamic and flexible configuration of sensing and processing functionalities available at the sensor node level. Many biophysical sensors and signal‐processing tasks are natively implemented and available to application developers. In addition, the SPINE framework has been carefully designed to allow for very easy integration ...