My publications


Enshaeifar S, Barnaghi P, Skillman S, Markides A, Elsaleh T, Acton T, Nilforooshan R, Rostill H (2018) Internet of Things for Dementia Care,IEEE Internet Computing 22 (1) pp. 8-17 IEEE COMPUTER SOC
In this paper we discuss a technical design and an
ongoing trial that is being conducted in the UK, called Technology
Integrated Health Management (TIHM). TIHM uses Internet of
Things (IoT) enabled solutions provided by various companies
in a collaborative project. The IoT devices and solutions are
integrated in a common platform that supports interoperable
and open standards. A set of machine learning and data analytics
algorithms generate notifications regarding the well-being of the
patients. The information is monitored around the clock by a
group of healthcare practitioners who take appropriate decisions
according to the collected data and generated notifications. In
this paper we discuss the design principles and the lessons that
we have learned by co-designing this system with patients, their
carers, clinicians, and also our industry partners. We discuss
the technical design of TIHM and explain why user-centred and
human-experience should be an integral part of the technological
Enshaeifar Shirin, Zoha Ahmed, Skillman Severin, Markides Andreas, Acton Tom, Elsaleh Tarek, Kenny M., Rostill H., Nilforooshan Ramin, Barnaghi Payam (2019) Machine learning methods for detecting urinary tract
infection and analysing daily living activities in people with dementia
PLOS One 14 (1) e0209909 pp. 1-22 PLOS
Dementia is a neurological and cognitive condition that affects millions of people around the world. At any given time in the United Kingdom, 1 in 4 hospital beds are occupied by a person with dementia, while about 22% of these hospital admissions are due to preventable causes. In this paper we discuss using Internet of Things (IoT) technologies and in-home sensory devices in combination with machine learning techniques to monitor health and well-being of people with dementia. This will allow us to provide more effective and preventative care and reduce preventable hospital admissions. One of the unique aspects of this work is combining environmental data with physiological data collected via low cost in-home sensory devices to extract actionable information regarding the health and well-being of people with dementia in their own home environment. We have worked with clinicians to design our machine learning algorithms where we focused on developing solutions for real-world settings. In our solutions, we avoid generating too many alerts/alarms to prevent increasing the monitoring and support workload. We have designed an algorithm to detect Urinary Tract Infections (UTI) which is one of the top five reasons of hospital admissions for people with dementia (around 9% of hospital admissions for people with dementia in the UK). To develop the UTI detection algorithm, we have used a Non-negative Matrix Factorisation (NMF) technique to extract latent factors from raw observation and use them for clustering and identifying the possible UTI cases. In addition, we have designed an algorithm for detecting changes in activity patterns to identify early symptoms of cognitive decline or health decline in order to provide personalised and preventative care services. For this purpose, we have used an Isolation Forest (iForest) technique to create a holistic view of the daily activity patterns. This paper describes the algorithms and discusses the evaluation of the work using a large set of real-world data collected from a trial with people with dementia and their caregivers.