Dr A Waraich, Dr P Fergus, Dr P Yang
No more applications being accepted
Funded PhD Project (European/UK Students Only)
About the Project
Background:
In the UK, the number of people living with self-limiting conditions, such as Dementia, Parkinson’s disease and depression is increasing. It is estimated that 700,000 people in the UK have a learning disability, and that 1 in 5 of those over the age of 65 will also develop dementia (Alzheimer’s Society, 2015). There is an urgent need for investigations into evidence-based approaches to ensuring quality of life of people with dementia. Currently, there is almost no evidence-based literature to guide implementation strategies to ensure early diagnosis or design optimal service provision for people with dementia. The resulting strain on national healthcare resources means that providing 24-hour monitoring for patients is a challenge. As this problem escalates, caring for an ageing population will become more demanding over the next decade. Furthermore, there is little research on how people with dementia interact with their environment, and how behaviour change principles can be used effectively with this population.
The rapid advance of Internet of Things (IoT) technologies grant us opportunities to build QoL profiles of individuals with increased reliability and validity by monitoring their lifelogging data captured by a variety of IoT assets (namely objects, sensors, mobile apps, web-objects, etc.) with constant connectivity and interaction in a pervasive network. The utilization of IoT technology in home based dementia care will enable accurate monitoring and deep analysis of home based dementia related behaviour including: gradual loss of memory, difficulty in performing familiar or complex tasks, changes in mood and disorientation, as well as considering sensitivity, social and emotional factors in working with patients at different stages of the illness.
With the implementation of Internet of Things (IoT) platforms and existing 2G communication platforms unobtrusive sensors offer an alternative and cost effective method for supporting independent living that could provide enhancements for Early Intervention Practices (EIP) In the UK, using IoT platforms.. This will allow sensors that collect data, such as movement data, to be connected to IoT networks to enable detailed around-the-clock monitoring of movements carried out by elderly and vulnerable patients in their homes. Habitual information about patients, such as what time they get up, how often they move and even detailed information such as when they are eating, sleeping and using the bathroom can be collected. Using IoT and communication technologies data can be captured and machine learning algorithms trained to understand detailed habits and routines (and any deviations from normal routines) using very small cheap low powered sensors.
Aim and objectives :
The main aim of this proposed research is to investigate IoT-enabled technologies for providing a cost-effective and non-intrusive intelligent system to monitor and analyse dementia related behaviour in a home setting. In particular, it will feature new techniques enabling simultaneous and long-term quantification of behaviour change related to dementia patients by integrating both wearable devices and low powered ambient sensors. The new wearable devices can also be used to automatically identify and mitigate issues related to poor information transference, interpretation (human to human and system to human), process management, cognitive capacity and patient related needs. The intelligent system will work with existing IoT platforms. . This will provide automated round the clock monitoring to promote safe independent living and preventative care with little cost to the NHS and social care services.
One of the main healthcare benefits of the system is the ability to facilitate a more timely response for Early Intervention Practice (EIP). There is no requirement for the patient to interact with the technology; they simply put on a sensor. Decisions will be made in the Cloud and alerts will be provided to mobile and infrastructure systems as and when issues are identified by machine learning models.
The objectives of the project are to:
1. To explore IoT methods to assess, monitor and measure the changes in behaviour and behavioural challenges in people with dementia;
2. To design a wearable intelligence-based behavioral change analysis model for use in home based dementia care.
3. To develop an interoperability hub by utilizing open platforms like FIWARE and UniversAAL to support the aggregation of lifelogging data from a variety of heterogeneous and newly developed IoT assets.
4. To carry out a thorough evaluation of the delivered system to validate its technical capacity and to examine its potential impact on future healthcare by working closely together with the end users.
5. To develop and evaluate training for direct care staff to implement behavioural programmes.