Department of Computer Science and Technology

Technical reports

Abstracting information on body area networks

Pedro Brandão

January 2012, 144 pages

This technical report is based on a dissertation submitted July 2011 by the author for the degree of Doctor of Philosophy to the University of Cambridge, Magdalene College.

DOI: 10.48456/tr-812


Healthcare is changing, correction, healthcare is in need of change. The population ageing, the increase in chronic and heart diseases and just the increase in population size will overwhelm the current hospital-centric healthcare.

There is a growing interest by individuals to monitor their own physiology. Not only for sport activities, but also to control their own diseases. They are changing from the passive healthcare receiver to a proactive self-healthcare taker. The focus is shifting from hospital centred treatment to a patient-centric healthcare monitoring.

Continuous, everyday, wearable monitoring and actuating is part of this change. In this setting, sensors that monitor the heart, blood pressure, movement, brain activity, dopamine levels, and actuators that pump insulin, ‘pump’ the heart, deliver drugs to specific organs, stimulate the brain are needed as pervasive components in and on the body. They will tend for people’s need of self-monitoring and facilitate healthcare delivery.

These components around a human body that communicate to sense and act in a coordinated fashion make a Body Area Network (BAN). In most cases, and in our view, a central, more powerful component will act as the coordinator of this network. These networks aim to augment the power to monitor the human body and react to problems discovered with this observation. One key advantage of this system is their overarching view of the whole network. That is, the central component can have an understanding of all the monitored signals and correlate them to better evaluate and react to problems. This is the focus of our thesis.

In this document we argue that this multi-parameter correlation of the heterogeneous sensed information is not being handled in BANs. The current view depends exclusively on the application that is using the network and its understanding of the parameters. This means that every application will oversee the BAN’s heterogeneous resources managing them directly without taking into consideration other applications, their needs and knowledge.

There are several physiological correlations already known by the medical field. Correlating blood pressure and cross sectional area of blood vessels to calculate blood velocity, estimating oxygen delivery from cardiac output and oxygen saturation, are such examples. This knowledge should be available in a BAN and shared by the several applications that make use of the network. This architecture implies a central component that manages the knowledge and the resources. And this is, in our view, missing in BANs.

Our proposal is a middleware layer that abstracts the underlying BAN’s resources to the application, providing instead an information model to be queried. The model describes the correlations for producing new information that the middleware knows about. Naturally, the raw sensed data is also part of the model. The middleware hides the specificities of the nodes that constitute the BAN, by making available their sensed production. Applications are able to query for information attaching requirements to these requests. The middleware is then responsible for satisfying the requests while optimising the resource usage of the BAN.

Our architecture proposal is divided in two corresponding layers, one that abstracts the nodes’ hardware (hiding node’s particularities) and the information layer that describes information available and how it is correlated. A prototype implementation of the architecture was done to illustrate the concept.

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BibTeX record

  author =	 {Brand{\~a}o, Pedro},
  title = 	 {{Abstracting information on body area networks}},
  year = 	 2012,
  month = 	 jan,
  url = 	 {},
  institution =  {University of Cambridge, Computer Laboratory},
  doi = 	 {10.48456/tr-812},
  number = 	 {UCAM-CL-TR-812}