Data transformation and query management in personal health sensor networks

Abstract

Sensor technology has been exploited in many application areas ranging from climate monitoring, to traffic management, and healthcare. The role of these sensors is to monitor human beings, the environment or instrumentation and provide continuous streams of information regarding their status or well being. In the case study presented in this work, the network is provided by football teams with sensors generating continuous heart rate values during a number of different sporting activities. In wireless networks such as these, the requirement is for methods of data management and transformation in order to present data in a format suited to high level queries. In effect, what is required is a traditional database-style query interface where domain experts can continue to probe for the answers required in more specialised environments. The challenge arises from the gap that emerges between the low level sensor output and the high level user requirements of the domain experts. This paper describes a process to close this gap by automatically harvesting the raw sensor data and providing semantic enrichment through the addition of context data.