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BioMed Research International
Volume 2015 (2015), Article ID 192454, 17 pages
Research Article

A Distributed Multiagent System Architecture for Body Area Networks Applied to Healthcare Monitoring

1Fundação Para a Ciência e a Tecnologia (FCT), Foundation for Science and Technology, 1249-074 Lisbon, Portugal
2Higher Technical School of Computer Engineering, University of Vigo, Polytechnic Building, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain
3INOV INESC INNOVATION, Institute of New Technologies of Leiria, 2411-901 Leiria, Portugal
4Computer Science and Communications Research Centre, School of Technology and Management, Polytechnic Institute of Leiria, 2411-901 Leiria, Portugal

Received 6 August 2014; Accepted 21 October 2014

Academic Editor: Juan M. Corchado

Copyright © 2015 Filipe Felisberto et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In the last years the area of health monitoring has grown significantly, attracting the attention of both academia and commercial sectors. At the same time, the availability of new biomedical sensors and suitable network protocols has led to the appearance of a new generation of wireless sensor networks, the so-called wireless body area networks. Nowadays, these networks are routinely used for continuous monitoring of vital parameters, movement, and the surrounding environment of people, but the large volume of data generated in different locations represents a major obstacle for the appropriate design, development, and deployment of more elaborated intelligent systems. In this context, we present an open and distributed architecture based on a multiagent system for recognizing human movements, identifying human postures, and detecting harmful activities. The proposed system evolved from a single node for fall detection to a multisensor hardware solution capable of identifying unhampered falls and analyzing the users’ movement. The experiments carried out contemplate two different scenarios and demonstrate the accuracy of our proposal as a real distributed movement monitoring and accident detection system. Moreover, we also characterize its performance, enabling future analyses and comparisons with similar approaches.