Postgraduate Research Opportunity

Wearable sensor-based rehabilitation exercise assessment platform for use in stroke rehabilitation

Details

Title: SCH14: Wearable sensor-based rehabilitation exercise assessment platform for use in stroke rehabilitation

Funding: Full time Home/EU fees and a stipend of £15,009 p.a. (terms & conditions)

Application deadline: 31 May 2019

Start date: October 2019

Duration: 3 years (full time)

Location: Colchester Campus

Based in: School of Computer Science and Electronic Engineering (in collaboration with the School of Sport, Rehabilitation and Exercise Sciences)

Overview

This highly interdisciplinary studentship brings together the expertise of the Post-stroke Rehabilitation with the Intelligent Embedded Systems and Environments research group also leveraging practical support from the Stroke Unit at Colchester Hospital, park of the East Suffolk and North Essex NHS Foundation Trust, which could in turn enable a significant scientific impact potential for this timely research endeavour.

We are looking for a highly motivated and interdisciplinary-minded student, who has an excellent computer science, electronics or related UG/MSc degree and is keen to work on relevant research in the area of embedded intelligent systems and machine learning algorithms for post-stroke rehabilitation.

The project

Stroke is a common disabling cerebrovascular disease, leaving its survivors with significant residual physical, cognitive, and psychological impairments.

In the UK stroke is also a leading cause of death and disability with about 32,000 stroke related deaths in England each year. According to recent statistics published by public health England, there are more than 100,000 stroke cases per year in the UK, which is equivalent to every stroke per five minutes, and there are over 1.2 million stroke survivors in the UK. Among them, 30% will likely have another stroke after the first doubling the risk of dying in the next two years.

Therefore, one of the major aims of patient care is to carry out rehabilitation processes to allow motor recovery of the body including constraint-induced movement therapy. This rehabilitation process needs to be supported with well-coordinated multidisciplinary stroke units as well as the provision of early supported discharge teams.

In the current rehabilitation process, there are persistent challenges in both patients and healthcare providers:

  1. Learning skills and motor control theories during rehabilitation interventions is not often cultivated effectively, as the inventions tend to be complex and involve many interconnected steps.
  2. The treatments are designed to address specific impairments, and from time to time, the progress of the activities is not adequate.
  3. It is a challenging endeavour to understand how patients precisely perform the rehabilitation tasks outside the hospital environment.
  4. During the post-stroke recovery, the living style, communication, and prevention of further strokes also demand significant improvements.

The main motivation of this project is the need for an effective post-stroke rehabilitation system that provides provide 24/7 assistance to stroke survivors as well as healthcare providers at home or in hospital environments.

Both stroke patients and healthcare providers could benefit from ICT-enabled artificial intelligent techniques, together with clear and on-time guidance coupled with support on how to improve the patient’s rehabilitation treatment and ultimately, prevent stroke recurrence.

Funding

The award consists of a full Home/EU fee waiver or equivalent fee discount for overseas students (further fee details), a doctoral stipend equivalent to the Research Councils UK National Minimum Doctoral Stipend (£15,009 in 2019-20), plus £2,500 training bursary via Proficio funding, which may be used to cover the cost of advanced skills training including conference attendance and travel.

Supervisors

Dr Xiaojun Zhai

Co-supervisor

School of Computer Science and Electronic Engineering, University of Essex

Dr Xiaojun Zhai is a Lecturer in the Embedded Intelligent Systems Laboratory at the University of Essex. He has been part of the EPSRC or EU funded projects for developing Embedded System and System-on-Chip (SoC) solutions for healthcare, machine learning and pattern recognition applications as well as other real-time critical applications. He has published papers in the area of reconfigurable computing, connected health applications, machine learning, Internet of Things, image and signal processing and their accelerations using multi-core and FPGAs/GPUs based systems.

Dr Victor Utti

Co-supervisor

School of Sport, Rehabilitation and Exercise Sciences, University of Essex

Dr Victor Utti a Lecturer in Neurological Physiotherapy at the University of Essex. Previously he has worked as a clinical physiotherapist in hospitals in Bristol and Plymouth. He has also worked in Africa in two different teaching hospitals managing neurological conditions. He is an Advanced Bobath Trained physiotherapist in upper limb rehabilitation post-neurological conditions and specialises particularly in the management and rehabilitation of patients with long term neurological conditions.

Professor Jo Jackson

Co-supervisor

School of Sport, Rehabilitation and Exercise Sciences, University of Essex

Professor Jo Jackson is the Director of Research and Professor of Physiotherapy at the University of Essex. She was previously the Dean of Health at the University with responsibility for contracting and commissioning activities with Health Education England in the Midlands and East. Current research of relevance includes gait, falling in older people and the use of mindfulness movement therapy for upper extremities in patients following a stroke. She is a member of the Association of Chartered Physiotherapists interested in Neurology and has presented her work at the national Physiotherapy UK conferences over a number of years.

Professor Klaus McDonald-Maier

Co-supervisor

School of Computer Science and Electronic Engineering, University of Essex

Professor Klaus D. McDonald-Maier is currently the Head of the Embedded and Intelligent Systems Laboratory. He is also the Chief Scientist with UltraSoC Technologies Ltd., the CEO of Metrarc Ltd., and a Visiting Professor with the University of Kent. His current research interests include embedded systems and system-on-chip design, security, development support and technology, parallel and energy-efficient architectures, computer vision, data analytics, and the application of soft computing and image processing techniques for real-world problems. He is a Senior Member of the IEEE, a member of VDE, a member of the Institute of Directors, and a Fellow of the HEA and the IET.

Criteria

The successful candidate should possess:

  • At a minimum, the successful applicant will have a good honours BSc degree in computer science, electronic engineering or other related subjects. An MSc with Merit or Distinction is desirable.
  • Strong analytical and mathematical skills are required, as well as good programming skills.
  • Knowledge of embedded systems, wearable sensors, machine-learning algorithms are essential.
  • Strong motivation to be involved in a project with a clinical background and data analysis skills.
  • Proficiency in spoken and written English.
  • Experience of conducting healthcare related experiments and knowledge of physiotherapy care are desirable.

How to apply

You can apply for this postgraduate research opportunity online.

Please include your CV, covering letter, personal statement, and transcripts of UG and Masters degrees in your application.

The University has moved to requiring only one reference for PhD applications and these can be received after a conditional offer has been made so the absence of these will not hold up the recruitment process.

Instruction to applicants

When you apply online you will be prompted to fill out several boxes in the form:

  • For "Course title" please put "PhD Electronic Systems Engineering"
  • For "Proposed research topic or area of research" please put the title of this studentship (SCH14: Wearable sensor-based rehabilitation exercise assessment platform for use in stroke rehabilitation)
  • For "If you have contacted a potential supervisor..." please put the name of the lead supervisor (Professor Shiyan Hu)

If you have any informal queries about this opportunity please email the lead supervisor, Professor Shiyan Hu (shiyan.hu@essex.ac.uk)

You can find the terms and conditions of this studentship here (PDF).