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Smart wearable devices for supporting elderly and frail individuals to live independently

   Centre for Health Activity and Rehabilitation Research

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  Dr P Koufaki, Prof H Yu, Prof H Yu  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

We aim to design, develop and evaluate a smart multi-sensor instrument that allows a personalised assessment of gait, stability and BP control. The data will be used to perform different analysis to show risks associated with PF and/or falls using AI approaches.

Approximately 28%-35% of people aged 65+ experience one fall every year1. Physical Frailty (PF) is one of the biological and behavioural risk factors linked with falls disability, social and mental dysfunction and increased morbidity.

The desire to live independently with advancing age, combined with multiple coexisting conditions and associated risks, may conflict with safety and ability to maintain other instrumental and leisure daily activities (ADLs). There is an ever growing need for sustainable and cost effective solutions and systems to support the world’s aging population in maintaining independent living and prevent the occurrence of common injuries associated with disability and frailty such as falls, bone fractures and hospital admissions. In this respect we are proposing to combine research expertise and clinical experience and networks of people from QMU and ENU universities  for a joint PhD programme of research. This project will aim to examine the dynamic interplay between physical movement during ADLs in the living environment and outcomes such as falls and related injuries as well as other health and well-being outcomes as agreed by the collaborating parties. Building on our previous work, we will implement new methodologies to record and monitor quantity and quality of human movement, incorporating data analysis, with state-of-art wearable devices, to offer novel insights into ADLs of elderly and frail individuals and the relationship to health and wellbeing outcomes.

Academic qualifications: A Master’s degree or a first degree (at least 2.1) ideally in Computing, or Computing Engineering, or Electronics and electrical engineering, or Robotics, or Mathematics or Health and Exercise related disciplines with a good understanding of  wearable sensors and  quantitative data analysis and experience with working with human participants in research or practice settings.


  • Experiences in sensor technology and data analysis
  • Good interpersonal skills
  • Component in statistics and data modelling
  • Knowledge of applied statistics
  • Good written and oral communication skills
  • Strong motivation, with evidence of independent research skills relevant to the project
  • Good time management

How to apply

Please visit our website to find information on how to apply.

Send enquiries to: Professor Hongnian Yu ([Email Address Removed]) and/or Dr Pelagia Koufaki ([Email Address Removed])


1. World Health Organisation (WHO), 2008. Global report on falls prevention in older age. Geneva: World Health Organization. (Accessible via: https://www.who.int/ageing/projects/falls_prevention_older_age/en/)
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