In this paper we show how we have used and adapted GT3 to support scalable and flexible remote medical monitoring applications on the Grid. We use two lightweight monitoring devices (a java phone and a wearable computer), which monitor blood glucose levels and ECG/SpO2 activity. We have connected those devices to the Grid by means of proxies, allowing those devices to be intermittently connected. The data from the devices is collected in a database on the Grid, and practitioners can obtain real time data or observe the patients historical data. Making remote data available to the Grid in order that a wider scientific community can access scientific data as quickly as possible often using varying quality communication services. Introduction The emergence of e-science and initiatives such as the UK e-science programme has been driven from the initial suggestion of “the Grid” as a distributed computing infrastructure for advanced science [1]. We have already seen considerable progress on the construction of such an infrastructure with software facilities such as the Globus Toolkit [2] becoming freely available based on the premise that high bandwidth communication allows storage and computational resources to be shared by a range of scientists accessing these services from their labs. Initiatives surrounding the GGF [3] such as OGSA [4] and work on the semantic grid [5] and OGSADAI [6] outline a clear position where powerful computational services and very large amounts of data are readily available to a distributed community of scholars. Making Grid facilities available to remote users when these need to be delivered across lower bandwidth communication using devices with significant display and processor limitations. Our particular interest is in solving the core technological problems involved in extending the grid by exploring these challenges within the medical domain. The need to make Grid facilities available “in the field” is particularly critical in the case of medical services where much of the day to day work of medicine centres on the patient requiring a number of medical professionals to correlate medical data with patient examination and observation. However, not all science takes place within the research lab and when we consider those areas where a significant portion of scientific activity takes place away from the lab a mismatch is evident between the provision of services enabled by current grid technology and the needs of the scientist in the field. Currently, the link between the field scientist undertaking remote work “on site” and their home lab is poorly supported and is often a significant bottleneck in the scientific process. Grid technology and medical devices The current trend towards Telemedicine and Telecare evident in the UK [7] has seen an explosion in the range of locations where advanced medical care needs to be delivered. E-medicine initiatives such as NHSdirect have illustrated the need to maximise the flexibility of delivery of health care. The ultimate goal is to increase the availability of medical care in order to both reduce the demands on hospital services and to improve the long term care and recovery of patients. Within the MIAS/ Equator Medical Devices Project we wish to address this potential scientific bottleneck by considering the development of mobile access to Grid services and how these services may be connected to a heterogeneous collection of mobile devices. Mobile access to grid facilities requires significant research to tack to two core research challenges. Existing trials in Telemedicine and Telecare such as those carried out by the Oxford centre for e-health [7], the Biomedical Informatics group at Nottingham University [8] and the Glasgow Royal Infirmary and Glasgow University [9] have demonstrated the feasibility of remotely monitoring patients as part of an overall care programme or as part of a clinical trial. However, these efforts have tended to be small scale in nature and have typically required the development of bespoke sensors and purpose build infrastructure for the logging of data for analysis. In this paper we present our initial work in creating a medical monitoring infrastructure that exploits standard grid software suitable for future clinical trials. The developed infrastructure allows lightweight medical devices to be made available on the grid as Grid Services. We have developed two distinct medical devices based on this infrastructure: a wearable medical monitoring jacket which independently delivers medical data to the Grid for analysis by researchers and clinicians; and a Java phone based blood glucose device which allows patients to self report medical information onto the Grid. Device 1: The Monitoring Jacket In developing our first medical device we have used a standard wearable platform (the Cyberjacket [10] developed at Bristol), which comes with wireless connectivity, and augmented it with three purpose built sensors: an ECG, an Oxygen Saturation Monitor and a temperature sensor. This augments the standard sensors on the Cyberjacket for position sensing (using GPS), and motion sensing (using accelerometers). Whereas current state of the art monitoring systems which typically require purpose built devices or significant customisation, the Cyberjacket has a modularised architecture. This means that health researchers can easily customise a jacket for their experiment. It also decreases the cost of units, as they are reusable in different configurations. Wearable architecture The wearable system consists of an ADS `bitsy’ processor unit (based on the StrongARM), with a custom 9-wire bus embedded in the fabric of the jacket. The bus provides sensors with power, ground, and communications via three serial links. Two links run at RS-232 levels, and are for dedicated RS-232 devices. Any commercially available device that runs RS-232 (such as a GPS) can be connected to one of these busses. The third bus runs at TTL level, at 4800 baud, and is used as a drop-link bus on which tens of devices (e.g. medical sensors) can be connected. In addition, the bitsy offers stereo sound I/O that can be used to give feedback to the wearer. The standard sensors can be attached to measure a range of activities: a compass mounted on a pair of headphones measures the direction in which the wearer is looking, and an accelerometer mounted vertically on the back can detect walking. A typical configuration of the medical wearable is shown in figure 1. Figure 1: A typical configuration of the wearable