Telecare and home healthcare services are an increasing healthcare research sector. In this field a novel approach is based on wearable sensor devices which provide a user-friendly acquisition of vital signs and allow the implementation of pervasive and continuous healthcare monitoring systems. The amazing amount of data continuously provided by sensors, poses challenging issues to the systems which are in charge of their collection and processing. In this paper we present a platform allowing real-time monitoring of biomedical and environmental parameters able to collect, store and process data gathered from a wide variety of sources. The platform presents a modular architecture that easily allows its extension with additional sensors. The main feature of the proposed system is the adoption of a complex event processor to correlate and manage events extracted from the collected data. This allows a wider and more accurate knowledge of the patient’s health status. A proof-of-concept implementation of the proposed platform has been realized and its implementation is detailed in this paper.
A healthcare real-time monitoring system for multiple sensors data collection and correlation
COPPOLINO, Luigi;ROMANO, LUIGI;
2009-01-01
Abstract
Telecare and home healthcare services are an increasing healthcare research sector. In this field a novel approach is based on wearable sensor devices which provide a user-friendly acquisition of vital signs and allow the implementation of pervasive and continuous healthcare monitoring systems. The amazing amount of data continuously provided by sensors, poses challenging issues to the systems which are in charge of their collection and processing. In this paper we present a platform allowing real-time monitoring of biomedical and environmental parameters able to collect, store and process data gathered from a wide variety of sources. The platform presents a modular architecture that easily allows its extension with additional sensors. The main feature of the proposed system is the adoption of a complex event processor to correlate and manage events extracted from the collected data. This allows a wider and more accurate knowledge of the patient’s health status. A proof-of-concept implementation of the proposed platform has been realized and its implementation is detailed in this paper.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.