Meet the team

Payam Barnaghi

"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 Applied to Medicine in the Department of Brain Sciences at Imperial College London. He was technical lead of the Department of Health/NHS Technology Integrated Health Management (TIHM) for Dementia project. Applying his technical research in machine learning and Internet of Things to healthcare, 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 leads 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 investigates if the environmental, physiological and behavioural data collected by the Healthy Home can sensitively monitor disease progression and can be used to detect and predict adverse health conditions. They focus on automatic detection of events such as the person becoming agitated, falls, or having sleep disturbances using AI and machine learning solutions. They investigate how machine learning can help identify and predict the specific patterns of those events – and also guide a person towards an appropriate solution, such as improving their hydration or revising their medication.

The scientists take great care with safeguarding the privacy and security of personal data. They 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 environmental and vital sign monitoring sensors, robotics, machine learning and data analytics in a digital platform to address major care needs and personalised healthcare for people living with dementia. The system includes in-home sensors, distributed edge computing, cloud data integration and machine learning algorithms. New technologies are evaluated and then integrated within this platform using 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 are trained to identify disease progression. Another focus is 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 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 models 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 and predict the risk of clinical events such as Urinary Tract Infections, 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 Samaneh Kouchaki, University of Surrey (Lecturer in Machine learning for Healthcare)
Anna Joffe (Senior Software Developer)
Severin Skillman, Imperial College London (Senior Software Developer)
Valentinas Janeiko (Software Developer)
Viktor Levine (Senior Software Developer)
Roonak Rezvani (PhD student)
Honglin Li (PhD student

4. Collaborations

Within UK DRI:

  • Prof David Sharp, UK DRI Care Research & Technology
  • Dr Hamed Haddadi, UK DRI Care Research & Technology 
  • Prof Derk-Jan Dijk, UK DRI Care Research & Technology 
  • Dr Ramin Nilforooshan, UK DRI Care Research & Technology
  • Prof Anne Skeldon, Department of Mathematics, University of Surrey 
  • Dr Gabriel Balmus, UK DRI at Cambridge

Beyond UK DRI: 

  • Prof Christine Miaskowski, University of California, San Francisco
  • Prof Neil Sebire, UCL/GOSH

5. Topics

Healthy home, AI and machine learning, sensors, personalised healthcare, digital platforms, Internet of Things, data privacy

6. Techniques

In-home sensors, deep learning, Bayesian methods, probabilistic machine learning, data analytics, distributed edge computing, cloud data integration, adaptive and continual machine learning models.

7. Key publications

Honglin Li, Payam Barnaghi, Shirin Enshaeifar, Frieder Ganz, "Continual Learning Using Bayesian Neural Networks", IEEE Transactions on Neural Networks and Learning Systems, 2020.

Roonak Rezvani, Payam Barnaghi, Shirin Enshaeifar, "A New Pattern Representation Method for Time-series Data", IEEE Transactions on Knowledge and Data Engineering (TKDE), 2020.

Shirin Enshaeifar, Payam Barnaghi, Severin Skillman, David Sharp, Ramin Nilforooshan, Helen Rostill, "A Digital Platform for Remote Healthcare Monitoring", Companion Proceedings of the Web Conference, 2020.

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.

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.

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