Roberta Giannantonio

Enabling Effective Programming and Flexible Management of Efficient Body Sensor Network Applications

Wireless body sensor networks (BSNs) possess enormous potential for changing peoples daily lives. They can enhance many human-centered application domains such as m-Health, sport and wellness, and human-centered applications that involve physical/virtual social interactions. However, there are still challenging issues that limit their wide diffusion in real life: primarily, the programming complexity of these systems, due to the lack of high-level software abstractions, and the hardware constraints of wearable devices. In contrast with low-level programming and general-purpose middleware, domain-specific frameworks are an emerging programming paradigm designed to fulfill the lack of suitable BSN programming support with proper abstraction layers. This paper analyzes the most important requirements for an effective BSN-specific software framework, enabling efficient signal-processing applications. Specifically, we present signal processing in node environment (SPINE), an open-source programming framework, designed to support rapid and flexible prototyping and management of BSN applications. We describe how SPINE efficiently addresses the identified requirements while providing performance analysis on the most common hardware/software sensor platforms. We also report a few high-impact BSN applications that have been entirely implemented using SPINE to demonstrate practical examples of its effectiveness and flexibility. This development experience has notably led to the definition of a SPINE-based design methodology for BSN applications. Finally, lessons learned from the development of such applications and from feedback received by the SPINE community are discussed.

From Modeling to Implementation of Virtual Sensors in Body Sensor Networks

Body Sensor Networks (BSNs) represent an emerging technology which has received much attention recently due to its enormous potential to enable remote, real-time, continuous and non-invasive monitoring of people in health-care, entertainment, fitness, sport, social interaction. Signal processing for BSNs usually comprises of multiple levels of data abstraction, from raw sensor data to data calculated from processing steps such as feature extraction and classification. This paper presents a multi-layer task model based on the concept of Virtual Sensors to improve architecture modularity and design reusability. Virtual Sensors are abstractions of components of BSN systems that include sensor sampling and processing tasks and provide data upon external requests. The Virtual Sensor model implementation relies on SPINE2, an open source domain-specific framework that is designed to support distributed sensing operations and signal processing for wireless sensor networks and enables code reusability, efficiency, and application interoperability. The proposed model is applied in the context of gait analysis through wearable sensors. A gait analysis system is developed according to a SPINE2-based Virtual Sensor architecture and experimentally evaluated. Obtained results confirm that great effectiveness can be achieved in designing and implementing BSN applications through the Virtual Sensor approach while maintaining high efficiency and accuracy.

SPINE: a domain-specific framework for rapid prototyping of WBSN applications

Wireless body sensor networks (WBSNs) enable a broad range of applications for continuous and real‐time health monitoring and medical assistance. Programming WBSN applications is a complex task especially due to the limitation of resources of typical hardware platforms and to the lack of suitable software abstractions. In this paper, SPINE (signal processing in‐node environment), a domain‐specific framework for rapid prototyping of WBSN applications, which is lightweight and flexible enough to be easily customized to fit particular application‐specific needs, is presented. The architecture of SPINE has two main components: one implemented on the node coordinating the WBSN and one on the nodes with sensors. The former is based on a Java application, which allows to configure and manage the network and implements the classification functions that are too heavy to be implemented on the sensor nodes. The latter supports sensing, computing and data transmission operations through a set of libraries, protocols and utility functions that are currently implemented for TinyOS platforms. SPINE allows evaluating different architectural choices and deciding how to distribute signal processing and classification functions over the nodes of the network. Finally, this paper describes an activity monitoring application and presents the benefits of using the SPINE framework. Copyright

DexterNet: An Open Platform for Heterogeneous Body Sensor Networks and its Applications

We present an open-source platform for wireless body sensor networks called DexterNet. The system supports real-time, persistent human monitoring in both indoor and outdoor environments. The platform utilizes a three-layer architecture to control heterogeneous body sensors. The first layer called the body sensor layer (BSL) deals with design of heterogeneous body sensors and their instrumentation on the body. At the second layer called the personal network layer (PNL), the body sensors on a single subject communicate with a mobile base station, which supports Linux OS and the IEEE 802.15.4 protocol. The BSL and PNL functions are abstracted and implemented as an open-source software library, called Signal Processing In Node Environment (SPINE). A DexterNet network is scalable, and can be reconfigured on-the-fly via SPINE. At the third layer called the global network layer (GNL), multiple PNLs communicate with a remote Internet server to permanently log the sensor data and support higher-level applications. We demonstrate the versatility of the DexterNet platform via several real-world applications.

Development of Body Sensor Network applications using SPINE

SPINE (signal processing in node environment) is a framework for the development of body sensor network (BSN) applications. It provides developers of signal processing algorithms with APIs and libraries of protocols, utilities and data processing functions. Hence, it offers application developers new abstractions that improve interoperability and allow to reduce development time. This paper presents the architecture and the capabilities of the SPINE framework, and shows its use in the development of a real-time activity monitoring system prototype.

Platform-independent development of collaborative wireless body sensor network applications: SPINE2

Rapid development of Wireless Body Sensor Network (WBSN) applications can be enabled by suitable domain-specific frameworks which are usually organized in two parts: a base-station-side (or coordinator) and a sensor-node-side. While the former can be based on the Java language so being highly portable, the latter is usually highly dependent on the exploited sensor platform. Available state of the art frameworks follow such an organization and, in particular, the current version of SPINE is based on TinyOS and can be only used to effectively develop collaborative WBSN applications for TinyOS-based sensor platforms. To develop SPINE-based applications for new sensor platforms, the SPINE framework should be re-implemented for each new sensor platform to be exploited. This not only increases development efforts but also enforces SPINE-oriented developers to become skilled on the low-level programming abstractions provided by a new employed sensor platform. In this paper we discuss issues related to platform-independent development of collaborative WBSN applications and, specifically, describe the requirements, architecture and first implementation experiences of SPINE2 which aims at reaching a very high platform independency and raising the level of the used programming abstractions by providing a task-oriented programming model. The paper also discusses how such a task-oriented model enables dynamic task assignment and holistic collaborative task execution also for resource-constrained environments such as tiny sensor nodes.

SPINE-HRV: A BSN-Based Toolkit for Heart Rate Variability Analysis in the Time-Domain

The Heart Rate Variability (HRV) is based on the analysis of the R-peak to R-peak intervals (RR-intervals) of the ECG signal in the time and/or frequency domains. Doctors and psychologists are increasingly recognizing the importance of HRV; in fact, a number of studies have demonstrated that patients with anxiety, phobias and post-traumatic stress disorder consistently show lower HRV, even when not exposed to a trauma related prompt. Importantly, this relationship existed independently of age, gender, trait anxiety, cardio-respiratory fitness, heart rate, blood pressure and respiration rate. In this paper, we present a toolkit based on body sensor networks (BSN) for the time-domain HRV analysis, namely SPINE-HRV (Signal Processing In Node Environment-HRV). The SPINE-HRV is composed of a wearable heart activity monitoring system to continuously acquire the RR-intervals, and a processing application developed using the SPINE framework. The developed system consists of a wireless chest band, a wireless wearable sensor node and a base station. The RR-intervals are processed using the SPINE framework at the base station side through a time-domain analysis of HRV. The analysis provides seven common parameters known in medical literature to help cardiologists in the diagnosis related to several heart diseases. In particular, SPINE-HRV is applied for stress detection of people during activities in their everyday life. Experimentations carried out by monitoring subjects in specific activities have shown the effectiveness of SPINE-HRV in detecting stress.

SPINE2: developing BSN applications on heterogeneous sensor nodes

Body sensor networks (BSNs) have great potential to enable a broad variety of assisted living applications such as health and activity monitoring, and emergency detection. Although several effective application development frameworks already exist for BSNs based on specific sensor platforms (e.g. CodeBlue, SPINE, Titan), effective methods for the platform-independent development of BSN applications are still missing. Such methods would enable rapid development of multi-platform applications and fast application porting from one platform to another. In this paper, we present SPINE2, an evolution of SPINE, which aims at reaching a very high platform independency and raising the level of the used programming abstractions by providing a task-oriented programming model. Furthermore, SPINE2 is exemplified through a case study related to human activity monitoring.

Enabling Multiple BSN Applications Using the SPINE Framework

Employment of BSN-based technologies in real world scenarios requires a flexible infrastructure at both hardware and software level. In this paper, we emphasize how the use of SPINE (Signal Processing In-Node Environment), a software framework for BSN, supports the development of heterogeneous health-care applications based on reusable subsystems. One of the main goal of SPINE is to provide a flexible architecture that can support variety of practical applications without the need for costly redeployment of the code running on sensor nodes. We also present a SPINE sensor node emulator that supports the first phase of the algorithm design, when the actual hardware devices may not be available. This approach can guide the choice of the required hardware (e.g. the sensors) to meet the application requirements based on the results obtained in the emulated environment. Such tool can simplify the research collaboration during the specification stage of a project, due to availability of a common (virtual) architecture.

Design, deployment and performance of a complete real-time ZigBee localization system

This paper proposes a complete system for nodes localization in a Wireless Sensor Network (WSN) based on the ZigBee standard. The system includes a real-time location engine, which adopts a Received Signal Strength Indicator (RSSI)-based localization algorithm, and three tools, namely an Environment Description Tool (EDT), a Channel Modeling Tool (CMT) and a Network Planning Tool (NPT), which enable efficient deployment and accurate operation. Experimental evaluations show how the system performs in a real environment and how the proposed approach improves localization accuracy.