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. 2014 Feb 25;14(3):3833-60.
doi: 10.3390/s140303833.

Sensor network infrastructure for a home care monitoring system

Filippo Palumbo  1 , Jonas Ullberg  2 , Ales Stimec  3 , Francesco Furfari  4 , Lars Karlsson  5 , Silvia Coradeschi  6
Affiliations

Sensor network infrastructure for a home care monitoring system

Filippo Palumbo et al. Sensors (Basel). .

Abstract

This paper presents the sensor network infrastructure for a home care system that allows long-term monitoring of physiological data and everyday activities. The aim of the proposed system is to allow the elderly to live longer in their home without compromising safety and ensuring the detection of health problems. The system offers the possibility of a virtual visit via a teleoperated robot. During the visit, physiological data and activities occurring during a period of time can be discussed. These data are collected from physiological sensors (e.g., temperature, blood pressure, glucose) and environmental sensors (e.g., motion, bed/chair occupancy, electrical usage). The system can also give alarms if sudden problems occur, like a fall, and warnings based on more long-term trends, such as the deterioration of health being detected. It has been implemented and tested in a test environment and has been deployed in six real homes for a year-long evaluation. The key contribution of the paper is the presentation of an implemented system for ambient assisted living (AAL) tested in a real environment, combining the acquisition of sensor data, a flexible and adaptable middleware compliant with the OSGistandard and a context recognition application. The system has been developed in a European project called GiraffPlus.

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Figures

Figure 1.
Figure 1.
The GiraffPlus system architecture.
Figure 2.
Figure 2.
The Giraff platform.
Figure 3.
Figure 3.
An in-depth view of the middleware component.
Figure 4.
Figure 4.
Interaction between components and buses. DVPIS, data visualization, personalization and interaction service.
Figure 5.
Figure 5.
The announce-listen protocol model.
Figure 6.
Figure 6.
The Android middleware architecture with component (a) and class (b) diagram.
Figure 7.
Figure 7.
A rule editor that is used to create and send queries to the context recognition system.
Figure 8.
Figure 8.
A timeline viewer showing the inference results from the query in Figure 8.
Figure 9.
Figure 9.
The map of one of the test site in Sweden showing the sensor positions.
Figure 10.
Figure 10.
The sensor integration mechanism.
See this image and copyright information in PMC

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