The Care Technology research theme (CARET) focuses on software technologies that support delivery of health and social care. This includes a wide range of approaches including:
Members of the research theme work on a number of different aspects of care technology:
This research is focused on long-term monitoring of people requiring support using ambient sensors. These are discreetly placed in the environment, are worn or are carried. The devices passively record data, detect subtle changes in activity signatures, and relate these changes to specific aspects of the user's condition. The current focus within the PAM project is on supporting patients with bipolar disorder at home. By detecting the onset of debilitating episodes, it is hoped that some patients will be helped to manage their condition more effectively.
This research is focused on providing support to midwives during childbirth and later in postnatal care. The SMILI project has looked at evaluating midwife support of the woman in childbirth. The PRAM project is creating and analysing models of how postnatal care is delivered.
This research is focussed on providing decision support tools to health care practitioners and patients. Research topics covered within this area include development of mobile decision support tools for paramedics, development of optimal strategies for scheduling chemotherapy, and prediction of the timing and types of side effects of chemotherapy treatment. As an example of this kind of research, the ASyMS project is working to help chemotherapy patients manage their symptoms more effectively. The aim is predict when symptoms will be at their worst and to provide advice on coping with them.
This research aims to support rigorous specification, analysis and testing of medical devices. The approach has been piloted in the field of radiology by the CONFORMED project. A range of formal models has been developed of radiotherapy accelerators. This has allowed the automatic generation of tests to validate correct operation of these safety-critical devices.
This research aims at exploiting inter-disciplinary advances in intelligent multi-modal signal processing (audio and video) for developing the next generation of hearing aids and cochlear implants for the hearing-impaired.
This research is focused on techniques to support delivery of health and social care in the home. The MATCH project is developing and integrating a range of approaches to this. Stirling's contribution is in the fields of home networks, medical device interfacing, policy-based (rule-based) management of care delivery, and flexible service discovery in a home care environment.
This research is adapting rule-based techniques originally conceived for system management. The pioneering work of the ACCENT project has been extended to support the special needs of managing care delivery to the home. An enhanced version of the APPEL policy language has been developed for defining policies appropriate to home care. This is supported by a user-friendly Policy Wizard for defining home care policies, and a Policy Server for executing them dynamically.
This research is using the techniques of Service-Oriented Architecture to underpin the delivery of health and social care in the home. OSGi (Open Services Gateway Initiative) is the preferred platform for supporting home care devices and services. Drivers (OSGi bundles) have been created for a variety of widely-used interfaces such SMS (Short Message Service), UPnP (Universal Plug and Play), X10 (mains-connected devices), and Visonic (wireless devices). A wide range of home care services has been created. New work is investigating the flexible integration of devices and services for rapid personalisation of care services.
This research has contributed to the ASyMS project (Advanced Symptom Management System). Symptom data is collected and recorded using a simple interface on a mobile phone. Based on statistical information, this can reassure the patient that their symptoms are typical, and can also provide some indication of the future pattern of symptoms. This has been trialled in chemotherapy, where patients may exhibit a variety of symptoms (e.g. headache, nausea) during cycles of drug treatment.
The research theme has contributed to this project, run by the Cancer Care Research Centre at Stirling. The main aim of this study has been to determine changes in symptom outcomes as a result of collecting symptom data via mobile phone, and giving predictions of likely future symptoms.
This project piloted the creation of formal models for safety-critical medical devices such as radiotherapy accelerators. The approach allows the automatic generation of tests to validate correct operation.
Stirling is the grant holder for a large European Science Foundation research network, funded under the EU Framework Programme 7. This involves 27 research institutes and laboratories from across Europe. The aim is to develop advanced speech and natural language processing algorithms for enabling the next generation of telecommunications services and products. Applications include remote health monitoring systems, interactive dialogue systems, intelligent avatars, and multi-modal aids for the hearing-impaired.
This project was concerned with investigation and development of computer decision support systems to assist with health decision making. This is predominantly being applied to diagnosis of dementia. However, the techniques and methodologies developed are flexible and can be applied to different medical areas and to non-medical domains.
MATCH is a collaborative research project focused on technologies for care at home. Stirling leads a consortium with the Universities of Dundee, Edinburgh and Glasgow as the research partners, along with eleven external partners. The project is developing a range of solutions in the fields of home care networks, lifestyle monitoring, speech communication, and multimodal user interfaces.
MEDIC is a collaborative research project with the Universities of Cranfield, Imperial College, Leeds, Queen's and Stirling. The aim is to develop a framework for mathematical and computational modelling that can capture the synergistic and spatiotemporal nature of an individual person, using this to recommend and monitor healthcare interventions.
PAM was a collaboration among the Universities of Southampton, Nottingham and Stirling. The project is investigating the feasibility of reducing the incidence of debilitating episodes among those with mental health problems. A non-intrusive approach is being developed, based on personalised ambient monitoring of patients in their homes.
POEMS is a collaboration among the Universities of Manchester, Sheffield and Stirling. This network project aims to be an accelerating mechanism for collaborations and research activity on predictive modelling for healthcare. These in turn will form the building blocks of a wider vision and research agenda on a national scale beyond the lifetime of the project.
PRAM is a collaboration among Computing Science, Management, and Nursing, Midwifery and Health at Stirling, with the cooperation of the Royal College of Midwives. The aim is to redesign the approach to postnatal care. The current system of care is being investigated in three diverse areas: Royal Berkshire Hospital in Reading, NHS Tayside and NHS Lanarkshire. The objective is to develop a new model of care which will be piloted in Scotland before being made available throughout the UK.
This project is part of the ASyMS project (Advanced Symptom Management System). The goal is to apply data mining to predict the likelihood of symptoms for patients undergoing chemotherapy. Analysis of historical data builds a picture of what factors influence the prevalence of certain side-effects, how often they occur, and the severity with which they occur. By providing information about a new individual, it is then possible to predict a pattern of side-effects. This allows the use of preventative techniques to alleviate these symptoms as far as possible.