This project is funded under the EPSRC WINES1 Programme.
Healthcare is coming under increasing pressure to improve the quality of care delivered to patients through effective prevention and post-operative care. This comes at a time when there is a need to curtail growth in healthcare spending fuelled by ageing populations, and the prevalence of obesity, diabetes, cancer and chronic heart and lung diseases.
The emphasis on sensor based measurements provides site-specific information from the body and in particular dynamic and quantitative differences between vascular and tissue compartments, thus generating entirely new data sets on which clinical decisions are based. Development in miniaturised wireless biosensors is set to reshape the common practice in clinical medicine especially for the prevention of terminal illness, monitoring the progression of chronic disease, and assessing post-operative care and body reaction to complex therapeutic drug regimes.
Thus far, new sensor technologies are increasingly being developed based on protein arrays and ion-sensitive FETs. These, together with established sensing technologies, are being used as critical sensing elements in the development of pervasive computing/sensing systems. These new sensors will be used within future ubiquitous healthcare systems to monitor patients as they maintain their normal everyday activities, in order to warn patients or healthcare workers of problems as well as collecting data for trend analysis and medical research.
The use of continuous monitoring circumvents the drawbacks of conventional diagnostics and monitoring (generally limited to brief time points and frequently unrepresentative physiological states or artificially introduced exercise tests), allowing both transient and progressive abnormalities to be reliably captured. This will be combined with new drug therapy and targeted delivery, minimally invasive intervention, and novel vessel prostheses.
The role of intelligent, context-aware sensor systems is to provide automated or semi-automated tools to support interventional management systems with detailed and co-ordinated information that can improve significantly the essential data available to the human observer, especially in accommodating problems of discontinuous data and data artefacts. This will enable better diagnosis and care of people (especially the ageing population), improved diagnostic technologies (more reliable, quicker, less restricting), and more reliable remote support of patients who are able to continue to live normal lives at home and at work.
Both short and longer term applications have been identified, including the monitoring of post-operative patients and those on complex therapeutic drug regimes (e.g. chemotherapy); patients with chronic disease (e.g. diabetes, heart or lung disease); and mental health patients whose behaviour in the community is dependent on their compliance with their drug regimes.
BioSensorNet will develop a new generation of intelligent biosensing networks that can
i) integrate local analogue signal processing with ultra-low power sensor interface and wireless data path;
ii) recognise the environment and physical context within which the signal is sensed; and
iii) form an autonomic system capable of self-configuring a network of sensors to provide reliable long-term adaptive sensing by fusing error-prone signals from individual sensors.
BiosensorNet builds on the success of the UbiMon project funded by DTI, and is aimed at addressing some of the long-term blue-sky research issues that can ultimately been brought to pervasive healthcare applications. |