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Wearable Sensor-Based Rehabilitation Exercise Assessment Platform for use in Stroke Rehabilitation


Project Description

Application deadline: Monday 28 October 2019
Start date: January 2020
Lead department: School of Computer Science and Electronic Engineering
Supporting department: School of Sport, Rehabilitation and Exercise Sciences

Studentship overview


This interdisciplinary studentship brings together expertise from the Post-stroke Rehabilitation with the Intelligent Embedded Systems and Environments research group also leveraging practical support from the Stroke Unit, Colchester Hospital, East Suffolk and North Essex NHS Foundation Trust. This collaboration will enable a significant scientific impact potential for this timely research endeavour. Therefore, we are looking for a highly motivated and interdisciplinary-minded student, who has an excellent computer science, electronics or related undergraduate or Masters degree and is keen to work on relevant research in the area of embedded intelligent systems and machine learning algorithms for post-stroke rehabilitation.

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. The main motivation of this project is the need for an effective post-stroke rehabilitation system that provides 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.

Primary supervisor


• Dr Xiaojun Zhai is a Lecturer in the Embedded Intelligent Systems Laboratory in the School of Computer Science and Electronic Engineering at the University of Essex.

Supporting supervisors


• Dr Victor Utti a Lecturer (Assistant Professor) in Neurological Physiotherapy in the School of Sport, Rehabilitation and Exercise Sciences (SRES) at the University of Essex.
• Professor Jo Jackson is the Director of Research and Professor of Physiotherapy in SRES at the University of Essex.
• Professor Klaus D. McDonald-Maier is currently the Head of the Embedded and Intelligent Systems Laboratory at the University of Essex.

Funding Notes

This award consists of a full Home/EU fee waiver or equivalent fee discount for overseas students (see www1.essex.ac.uk/fees-and-funding/research/default.aspx for further 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.

International students can apply for this studentship but would have to pay the difference between Home/EU and international student fees.

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