"The technologies developed in this programme will enable people with dementia to live independently at home whilst not sacrificing their care." Payam Barnaghi
UK DRI Group Leader
An expert in artificial intelligence and big data, Prof Payam Barnaghi is Professor of Machine Intelligence at the University of Surrey and technical lead of the Department of Health/NHS Technology Integrated Health Management (TIHM) for Dementia project. Applying his background to cyber-physical and social systems, Payam has won several awards including the HSJ 2018 Award for Improving Care with Technology and an IEEE Outstanding Leadership Award in 2017. Joining the UK DRI Care Research & Technology Centre at Imperial as Deputy Director, he will lead ground-breaking research into the ‘Healthy Home’, using technology to maintain independence for people with dementia.
1. At a glance
Creating a ‘Healthy Home’ environment for people living with dementia
Prof Payam Barnaghi is combining engineering and technological innovation to produce a secure ‘Healthy Home’ system that will incorporate a range of sensors to collect data from people living with dementia in unprecedented detail. By integrating robotics and sophisticated data-analytics, this system will provide a test-bed for new approaches to address major care needs and provide personalised healthcare for these individuals.
The team will investigate if the environmental, physiological and behavioural data collected by the Healthy Home can sensitively monitor disease progression. They will also focus on refining its ability to automatically detect events such as the person becoming agitated, falling down, or having sleep disturbances using AI and machine learning solutions. They will then investigate if machine learning can help identify and predict the specific causes of that behaviour – and also guide a person towards an appropriate solution, such as improving their hydration or revising their medication.
The scientists will take great care with safeguarding the privacy and security of personal data. They will build on their existing ‘Databox’ system to develop improved ways of storing and analysing data within the home. Although they will allow the limited sharing of data centrally, this will only be after any sensitive information is removed.
2. Scientific goals
Prof Payam Barnaghi and his team will combine their engineering and technological innovation within a ‘Healthy Home’ system that integrates biosensors, robotics, machine learning and data-analytics in a single framework to address major care needs and personalised healthcare for people living with dementia. The system will include in-home sensors, distributed edge computing, cloud data integration, machine learning algorithms, and utilise the award-winning Technology Integrated Health Management (THIM) for dementia platform as a foundation for this work. New technologies will be evaluated and then integrated within THIM, significantly extending its capability through novel biosensors, robotics and new clinical analytics.
Identifying disease progression and important clinical events: In people with preclinical or incipient dementia, the researchers will test whether disease progression can be most sensitively measured using behavioural and physiological data provided by the Healthy Home, and machine-learning algorithms will be trained to identify disease progression. Another focus will be on refining the automatic identification of clinical events, such as agitation, sleep disturbance and falls; the rich home data that they will collect provides the opportunity to investigate physiological or environmental causes for these clinical events. The team will define general network descriptions that capture the potential causal interactions, as well as investigating the most sensitive way to identify specific causes for an individual. For example, a range of factors might cause agitation, including dehydration, sleep disturbance or change to medication. Their importance will vary and personalised predictions about causation should ultimately be able to guide simple interventions such as improving hydration or revising medication. This process is routine in the assessment of individuals attending hospital and the aim is to automate elements of this process using machine-learning methods that combine knowledge of the likely causes of a clinical event with personalised home monitoring data.
Data Security and Privacy: The team will develop solutions that address data security and privacy concerns, building on the novel 'Databox' system. This home-based, personal networked Edge device, provides low-cost distributed analytics for the local processing, abstraction and de-identification of data. Combined with federated learning principles, this allows machine-learning approaches to be applied without the need to centralise data on the cloud. This safeguards privacy by flexibly allowing personal data to either be analysed in the home or to be changed into a form that removes sensitive information prior to central sharing.
Main objectives and research goals:
1. To produce a cost-effective and secure Healthy Home system that integrates diverse technology to address major care needs.
2. To use machine-learning methods to (i) measure disease progression; and (ii) identify clinical events such as sleep disturbance and agitation, and identify their causes.
3. Develop solutions to address data security and privacy concerns, building on the ‘Databox’ system.
3. Team members
Dr Shirin Enshaeifar, University of Surrey (Lecturer in Machine learning for Healthcare)
Severin Skillman, University of Surrey (Senior Software Developer)
Within UK DRI:
- Prof David Sharp, UK DRI at Care Research & Technology at Imperial
- Dr Hamed Haddadi, UK DRI at Care Research & Technology at Imperial
- Prof Derk-Jan Dijk, UK DRI at Care Research & Technology at Imperial
Beyond UK DRI:
- Dr Ramin Nilforooshan, Surrey and Borders Partnership NHS Trust
- Prof Anne Skeldon, Department of Mathematics, University of Surrey
Healthy home, sensors, personalised healthcare, machine learning, Internet of Things, data privacy
In-home sensors, robotics, data analytics, distributed edge computing, cloud data integration, machine learning algorithms
7. Key publications
Shirin Enshaeifar, Ahmed Zoha, Andreas Markides, Severin Skillman, Sahr Thomas Acton, Tarek Elsaleh, Mark Kenny, Helen Rostill, Ramin Nilforooshan, Payam Barnaghi, "Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia", PLoS ONE 14(1): e0209909.
Nikolaos Papachristou, Payam Barnaghi, Bruce Cooper, Kord M Kober, Roma Maguire, Steven M Paul, Marilyn Hammer, Fay Wright, , Jo Armes, Eileen P Furlong, Lisa McCann, Yvette P Conley, Elisabeth Patiraki, Stylianos Katsaragakis, Jon D Levine, Christine Miaskowski, "Network Analysis of the Multidimensional Symptom Experience of Oncology", Scientific Reports, volume 9, article number: 2258, 2019.
Shirin Enshaeifar, Ahmed Zoha, Andreas Markides, Severin Skillman, Sahr Thomas Acton, Tarek Elsaleh, Masoud Hassanpour, Alireza Ahrabian, Mark Kenny, Stuart Klein, Helen Rostill, Ramin Nilforooshan, Payam Barnaghi, "Health management and pattern analysis of daily living activities of people with Dementia using in-home sensors and machine learning techniques", PLoS ONE 13(5): e0195605, 2018.
Shirin Enshaeifar, Payam Barnaghi, Tarek Elsaleh, Andreas Markides, Severin Skillman, Thomas Acton, Ramin Nilforooshan, Helen Rostill, "Internet of Things for Dementia Care", IEEE Internet Computing, 2017.
Aurora González Vidal, Payam Barnaghi, Antonio F. Skarmeta, "BEATS: Blocks of Eigenvalues Algorithm for Time series Segmentation", IEEE Transactions on Knowledge and Data Engineering (TKDE), 2018.
Yasmin Fathy, Payam Barnaghi, Rahim Tafazolli, "An Online Adaptive Algorithm for Change Detection in Streaming Sensory Data", IEEE Systems Journal, 2018.