A key factor for the success of such systems is the smooth, easy and reliable integration of all hardware components and the integration of the hardware components with the higher level of the system performing monitoring and reasoning on the data. This is especially of importance in home care monitoring, as the system needs to be deployed in a private home and, therefore, requires a quick installation, adaptability to changes in sensor configuration and reliability over long periods of time. The GiraffPlus project is an EU FP7-funded project in which we develop and thoroughly evaluate a complete system that collects daily behavioral and physiological data from distributed sensors, performs context recognition, a long-term trend analysis and presents the information via a personalized interface.
GiraffPlus supports social interaction between primary users (elderly) and secondary users (formal and informal caregivers), thereby offering an immediate and obvious benefit, which makes the system attractive and worth using. The GiraffPlus system is named after one of its components: the Giraff telepresence robot. The robot uses a Skype-like interface, allowing caregivers to virtually visit an elderly person in the home. The GiraffPlus system also includes a network of sensors placed in the home. These include physiological sensors for, e.g., weight, blood pressure and pulse oximetry, and environmental sensors. Data from these sensors are stored in a database and processed by an advanced context recognition system, which uses constraint-based temporal reasoning in order to detect events on-line or perform inferences about long-term behaviors and trends.
Secondary users can access the data and events remotely through personalized services: users may have access to different data, may be interested in different events and may want the information presented in different ways.This paper focuses on the architecture of the GiraffPlus system and, in particular, the hardware components and their integration via a middleware infrastructure. It first presents related work, then an overview of the system, the hardware and software components (and, in particular, the middleware), the data storage, the context recognition and configuration planning. Finally, the paper reports on the testing of the system and presents the conclusions.
The key contribution of the paper is the presentation of an implemented system for ambient assisted living (AAL) tested in a real environment. It combines the acquisition of sensor data via a flexible and adaptable Brefeldin_A middleware with high-level reasoning and, in particular, context recognition.2.?Related WorkThe increase in lifestyle related diseases, together with an aging population, are important driving forces to develop systems that facilitate the monitoring of health status independent of location: at home, at work or in the hospital.