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A distributed multiagent system architecture for body area networks applied to healthcare monitoring.

Felisberto F, Laza R, Fdez-Riverola F, Pereira A - Biomed Res Int (2015)

Bottom Line: In the last years the area of health monitoring has grown significantly, attracting the attention of both academia and commercial sectors.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.

View Article: PubMed Central - PubMed

Affiliation: Fundação Para a Ciência e a Tecnologia (FCT), Foundation for Science and Technology, 1249-074 Lisbon, Portugal ; Higher Technical School of Computer Engineering, University of Vigo, Polytechnic Building, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain.

ABSTRACT
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.

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External process for calibrating the nodes used in the implemented BAN.
© Copyright Policy - open-access
Related In: Results  -  Collection


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fig1: External process for calibrating the nodes used in the implemented BAN.

Mentions: In particular, this project has strict rules of using noninvasive methods to monitor the user, so in no way could the sensors be physically attached to the human body. This requirement introduces a variable related to the small differences in the node position every time the user puts on the nodes. In order to be possible to precisely evaluate data between usages and specially to be able to viably compare data between users, it was mandatory to introduce an external system capable of correctly evaluating the body part position against its own known referential. As the external system is only necessary during the initial setup, it does not introduce constraints in terms of user's spatial movement or to the user's privacy. For the current version of the architecture the external module used to setup the whole system was the Microsoft Kinect SDK (see Figure 1).


A distributed multiagent system architecture for body area networks applied to healthcare monitoring.

Felisberto F, Laza R, Fdez-Riverola F, Pereira A - Biomed Res Int (2015)

External process for calibrating the nodes used in the implemented BAN.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4385603&req=5

fig1: External process for calibrating the nodes used in the implemented BAN.
Mentions: In particular, this project has strict rules of using noninvasive methods to monitor the user, so in no way could the sensors be physically attached to the human body. This requirement introduces a variable related to the small differences in the node position every time the user puts on the nodes. In order to be possible to precisely evaluate data between usages and specially to be able to viably compare data between users, it was mandatory to introduce an external system capable of correctly evaluating the body part position against its own known referential. As the external system is only necessary during the initial setup, it does not introduce constraints in terms of user's spatial movement or to the user's privacy. For the current version of the architecture the external module used to setup the whole system was the Microsoft Kinect SDK (see Figure 1).

Bottom Line: In the last years the area of health monitoring has grown significantly, attracting the attention of both academia and commercial sectors.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.

View Article: PubMed Central - PubMed

Affiliation: Fundação Para a Ciência e a Tecnologia (FCT), Foundation for Science and Technology, 1249-074 Lisbon, Portugal ; Higher Technical School of Computer Engineering, University of Vigo, Polytechnic Building, Campus Universitario As Lagoas s/n, 32004 Ourense, Spain.

ABSTRACT
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.

Show MeSH