Search or filter publications

Search publications Search

Filter by type:

Filter by publication type

• Book
• Book chapter
• Conference paper
• Journal article
• Patent
• Report
• Software

Filter by year:

Filter from 2024202320222021202020192018 to Filter to 2024202320222021202020192018

---

Search results

• Journal article
  Enshaeifar S, Zoha A, Skillman S, Markides A, Acton ST, Elsaleh T, Kenny M, Rostill H, Nilforooshan R, Barnaghi Pet al., 2019,

  Machine learning methods for detecting urinary tract infection and analysing daily living activities in people with dementia

  , PLoS One, Vol: 14, ISSN: 1932-6203

  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 lar

  • Abstract
  • Publisher Web Link
  • Author Web Link
  • Open Access Link
  • Cite
• Conference paper
  Nilforooshan R, Rostill H, Barnaghi P, Enshaeifar S, Markides A, Skillman S, Kenny Met al., 2019,

  Transforming care for people with dementia using the Internet of Things

  , Publisher: UBIQUITY PRESS LTD, ISSN: 1568-4156
  • Author Web Link
  • Cite
  • Citations: 3
• Conference paper
  De Marcellis A, Di Patrizio Stanchieri G, Palange E, Faccio M, Constandinou TGet al., 2018,

  An ultra-wideband-inspired system-on-chip for an optical bidirectional transcutaneous biotelemetry

  , IEEE Biomedical Circuits and Systems (BioCAS) Conference 2018, Publisher: IEEE, Pages: 351-354

  This paper describes an integrated communicationsystem, implementing a UWB-inspired pulsed coding technique,for an optical transcutaneous biotelemetry. The system consistsof both a transmitter and a receiver facilitating a bidirectionallink. The transmitter includes a digital data coding circuit and iscapable of generating sub-nanosecond current pulses and directlydriving an off-chip semiconductor laser diode including all biasand drive circuits. The receiver includes an integrated compactPN-junction photodiode together with signal conditioning, de-tection and digital data decoding circuits to enable a high bitrate, energy efficient communication. The proposed solution hasbeen implemented in a commercially available 0.35μm CMOStechnology provided by AMS. The circuit core occupies a compactsilicon footprint of less than 0.13 mm2(only 113 transistors and1 resistor). Post-layout simulations have validated the overallsystem operation demonstrating the ability to operate at bit ratesup to 500 Mbps with pulse widths of 300 ps with a total powerefficiency (transmitter + receiver) lower than 74 pJ/bit. Thismakes the system ideally suited for demanding applications thatrequire high bit rates at extremely low energy levels. One suchapplication is implantable brain machine interfaces requiringhigh uplink bitrates to transmit recorded data externally througha transcutaneous communication channel.

  • Abstract
  • Publisher Web Link
  • Open Access Link
  • Cite
• Journal article
  Li H, Enshaeifar S, Skillman S, Markides A, Kenny M, Sharp D, Rostill H, Nilforooshan R, Barnaghi Pet al.,

  A Semi-Supervised Machine Learning Model for Risk Analysis of Urinary Tract Infections in People with Dementia

  • Cite

This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.