Capturing personal health data from wearable sensors

Recently, there has been a significant growth in pervasive computing and ubiquitous sensing which strives to develop and deploy sensing technology all around us. We are also seeing the emergence of applications such as environmental and personal health monitoring to leverage data from a physical world. Most of the developments in this area have been concerned with either developing the sensing technologies, or the infrastructure (middleware) to gather this data and the issues which have been addressed include power consumption on the devices, security of data transmission, networking challenges in gathering and storing the data and fault tolerance in the event of network and/or device failure. Research is focusing on harvesting and managing data and providing query capabilities

HealthSense: an application for querying raw sensor data

New sensing technologies and the decreasing cost of Information and Communication Technologies (ICTs) make possible the development of electronic Health (eHealth) monitoring systems. The challenges of such systems include the representation of data extracted from various sensor devices by knowledge workers through semantic enrichment and integration. Also, the data must be stored in a format suitable for querying and further analysis. This paper describes the demonstration of the HealthSense system which captures and queries personal health data extracted from wearable sensors

Integrating sensor streams in pHealth networks

Personal Health (pHealth) sensor networks are generally used to monitor the wellbeing of both athletes and the general public to inform health specialists of future and often serious ailments. The problem facing these domain experts is the scale and quality of data they must search in order to extract meaningful results. By using peer-to-peer sensor architectures and a mechanism for reducing the search space, we can, to some extent, address the scalability issue. However, synchronisation and normalisation of distributed sensor streams remains a problem in many networks. In the case of pHealth sensor networks, it is crucial for experts to align multiple sensor readings before query or data mining activities can take place. This paper presents a system for clustering and synchronising sensor streams in preparation for user queries

Synchronizing sensed data in team sports

In this article we will be discussing the synchronization of sensor data in team sports. Synchronization allows us to use more expressive queries, to query across all participants in a given activity and to potentially discover new knowledge from the semantically enriched data. A collaborative research effort between groups working on data management and on health and human performance (both at Dublin City University) involved a series of experiments using wearable sensors during team games and the capture and querying of sensed dat

Synchronizing sensed data in team sports

In this article we will be discussing the synchronization of sensor data in team sports. Synchronization allows us to use more expressive queries, to query across all participants in a given activity and to potentially discover new knowledge from the semantically enriched data. A collaborative research effort between groups working on data management and on health and human performance (both at Dublin City University) involved a series of experiments using wearable sensors during team games and the capture and querying of sensed dat