Integrated technologies of care

What are our intentions?

This research programme will develop practical, care-driven technologies that are fit for people in later life. In particular, this means exploring and developing data-driven IoT platforms that can produce accurate data about instant events (e.g. vital signs and serious incidents such as falls), short-term activities (e.g. those of daily living) and long-term pursuits (e.g. physical and mental activities over weeks and months) in order to extract (predictive) information and patterns that can be used, among other things, for effective interventions and the prevention of adverse outcomes. 

We will create a community for collaboration that will provide the long-term context for the development and evaluation of multiple new technologies of care in a collaboration between academics from several disciplines, people in later life and their families, health and social care professionals, and businesses in this market.  The collaboration will be formed around the implementation of routine monitoring in a highly vulnerable community in order to detect deterioration early, with a process to inform the focus of future work. We will both make a contribution to knowledge and practice, and support long-term development of new technical innovation.

For this purpose, we identify three main research goals:

1. Implementing routine physiological monitoring in a home setting.
2. Development and implementation of additional sensing methods and devices.
3. Development of decision support and intervention management.  

How will we achieve this?

The overall design of this work package is based on a set of 7 linked and complementary tasks that take advantage of an opportunity to generate sensors and technologies that complement care provided to people in later life. The research, which spans engineering and informatics, will use a variety of methods to produce platforms that combine elements such as multi-modal sensor integration, data analysis and fusion, AI-based decision and prediction modelling and assistive technologies to facilitate care for people in later life. For this work, we have established partnerships with a number of health and care partners who have agreed to provide expertise, data and other resources towards the development and validation of the outputs from the project. We will also work with colleagues in other work packages to prioritise the development of additional sensing parameters, AI-based decision support and assistive technologies, thereby ensuring we develop future-facing, innovative technologies that deliver the right care to people in later life. 

Task 1: Comprehensive Review 

The aim of this task is to carry out comprehensive reviews of the sensors and devices used for the monitoring of physiological parameters and activities and of the assistive technologies used for their care. These reviews will mainly inform the research being done in Tasks 2 and 7 but we expect them to be relevant to other tasks in different work packages. 

Task 2: Devices and Sensing

This task will investigate the use of a number of available sensing methods to validate their effectiveness in capturing key physiological parameters, as well as, detect and predict the onset of falls through a number of key parameters. In the first instance, sensors will be embedded into fixed objects. Another approach would be to investigate integration of these sensors with personal items, such as bedsheets, clothes, patches, and other wearable items that are available in the care environment.  

Task 3: Data Aquisition, Management and Fusion 

In a continuous monitoring programme, it is important to process data online and in a timely manner in order to capture events such as falls or domestic accidents, that occur either unexpectedly or are triggered by a sequence of events, behaviours and external factors taking place over short intervals. This task is focused on maintaining a summary of the data collected via the multi-sensory platform in Task 2 in order to streamline their use in Tasks 4 and 5.  

Task 4: Process, Performance and Predictive Modelling

This task will develop digitised models of care to allow for meaningful understanding and decision support. This includes models that capture knowledge about care pathways, guidelines, and practices, as well as individual needs and circumstances. These will be used to inform the new models of care work package. Explainable models using AI on existing data to produce alerts and insights towards meaningful interventions. Finally, we will use these digital models to provide predictive performance measures under different circumstances and ageing population trends.

Task 5: Behavior Modelling and Decision Making 

This task will involve the creation of human-decision making models that can help with the workflows from Task 4. It will also build data-driven decision models that capture causal behaviour in order to provide coaching, e.g. helping the person better cope with exercise or medication regimes, and advice to the carer, regarding how activity variables are influencing measured outcomes. 

Task 6: Infrastructure 

In this task, we will build proof-of-concept systems for the different features developed across Tasks 2-5. More specifically, we will develop prototypes aimed at:

1. IoT and data management
2. The care receiver
3. The care provider. 

In a later phase, we will seek to use our prototypes as a testbed for our work in real environments. Issues of robustness, security, privacy, and always-on availability will be key considerations towards effective, AI-supported decision making and interaction at both the customer and carer ends. 

Task 7: Intervention and Evaluation 

This task will initially involve the development and evaluation of our technology as part of the Bayes Centre/School of Informatics ‘Living Lab’.  It will involve a close collaboration with ACRC colleagues and our health and social care collaborators to leverage their understanding of the older person. This will ensure a user-centred, iterative design of our technology and of its evaluation. In a later phase, in collaboration with other work packages and our care partners, we will explore the possibility of setting up a real-world case-study to investigate a complex intervention and its evaluation. 

Meet the Team: Integrated Technologies of Care

Workpackage Lead - Professor Jacques Fleuriot

Jacques Fleuriot is Personal Chair of Artificial Intelligence and the Director of the Artificial Intelligence and its Application in the School of Informatics. His research focuses on AI modelling, which spans areas such as formal verification, process modelling, and explainable AI in healthcare and other complex domains.  

Find out more about Jacques Fleuriot on their profile page

Workpackage Lead - Professor Tughrul Arslan 

Tughrul Arslan is a professor in the School of Engineering. His research focuses on the development of intelligent power efficient sensing systems that are non-contact, portable, and/or wearable for a range of medical conditions. He has authored over 500 refereed articles and 25 patents, most of which have been licensed/sold to spinout or tier1 companies.

Find out more about Tughrul Arslan on their profile page

Academic Lead - Professor Robert B Fisher

Robert B. Fisher FIAPR, FBMVA (PhD Edinburgh, 1987) has a Personal Chair in Computer Vision. His research covers many topics in computer vision, specialising in 3D computer vision and its application (300+ peer-reviewed scientific articles). 

Find out more about Robert on their profile page 

Academic Lead - Professor Jane Hillston

Jane Hillston FRSE MAE is a leading expert in quantitative formal methods which support logic-based analysis of stochastic dynamic systems.  Her work has been applied in a variety of domains ranging from intracellular signalling systems, cloud computing, to smart city transport systems.

Find out more about Jane Hillston on their profile page

Academic Lead - Dr. Srinjoy Mitra 

Srinjoy Mitra is a Senior Lecturer with extensive experience in custom integrated circuit design for ultra-low power sensor interfaces, particularly for biomedical applications. He has led industry sponsored and publicly funded projects related to ambulatory medical devices.

Find out more about Srinjoy Mitra on their profile page

Academic Lead - Dr. Nick Polydorides

Nick Polydorides is a Reader in computational engineering, specialising in data science. He is the Head of the Digital Communications Research Institute in the School of Engineering. He works on data sketching algorithms for real time simulation and data compression. 

Find out more about Nick Polydorides on their profile page

Academic Lead - Professor Subramanian Ramamoorthy

Subramanian Ramamoorthy is Professor and Personal Chair of Robot Learning and Autonomy, in the School of Informatics, University of Edinburgh where he is the incoming Director of the Institute of Perception, Action and Behaviour.  His work is focussed on achieving safe autonomy for robotics in human-centered environments. He is Vice President - Prediction and Planning at FiveAI, a UK-based startup company developing autonomous vehicles technology. 

Find out more about Subramanian on their profile page

Research Associate - Dr. Ricardo Contreras

Ricardo is a Research Associate at ACRC. His research interests include modelling and monitoring constraints of dynamic compositions and data processing, with a focus on older adults in different contexts. He has several years of experience in industry working in the health sector, including the development of prototypes for the diagnosis of Alzheimer’s disease.

Find out more about Ricardo on their profile page.

Research Associate - Dr Nuša Farič 

Nuša studied at the University of Glasgow (BSc Hons Psychology), UCL (MSc Health Psychology), and PhD (Health Psychology and Informatics for the UCL Institute of Health Informatics). She has experience working in academia, private and public healthcare, medical communications industry and consumer health, in multidisciplinary teams using mixed-methods approaches and has interests in psychology, health psychology, AI, femtech, women's health, health content on Wikipedia and innovative health solutions.

Her role is a  joint role across WP4 and WP6.