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Deciphering how stroke impairs spatial navigation during walking


   Department of Computer Science

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  Dr O Akanyeti  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

The UKRI CDT in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC) aims at forming the next generation of AI innovators across a broad range of STEMM disciplines. The CDT provides advanced multi-disciplinary training in an inclusive, caring and open environment that nurture each individual student to achieve their full potential. Applications are encouraged from candidates from a diverse background that can positively contribute to the future of our society. 

The UK Research and Innovation (UKRI) fully-funded scholarships cover the full cost of tuition fees, a UKRI standard stipend of £15,921 per annum and additional funding for training, research and conference expenses. The scholarships are open to UK and international candidates.

Closing date for applications is 12 February 2022. For further information on how to apply please click here and select the "UKRI CDT Scholarship in AIMLAC" tab.

Project Overview

Stroke disrupts various cognitive processes including memory formation, orientation, executive function, attention, and judgement (Lugtmeijer et al., 2021). While relationship between post-stroke cognitive impairments and functional outcomes is well documented, much less is known about to what extent these impairments affect people’s ability to navigate in the real world, from low level locomotor control (e.g., maintaining balance) to high level path planning (e.g., detection and avoiding obstacles) (van der Ham et al., 2013). Diminished navigational skills has serious repercussions on independence and quality of life by increasing the risk of injury and falling.

The new PhD student will combine experimental tools including brain imaging, wearable eye tracking and motion analysis, and virtual reality to study how stroke (compared to age-matched controls) impairs spatial navigation and to create data-driven computational models/algorithms to elucidate stroke-impaired navigation strategies. These models/algorithms should be human-interpretable and biologically plausible so that they can help scientists generate new research hypotheses about stroke and navigation as well as rehabilitation interventions. While developing and testing these models, the student will use state-of-the-art system identification and machine learning approaches (e.g., NARMAX, fuzzy logic and evolutionary artificial neural networks).

The student will be part of a vibrant and multi-disciplinary research group (Aberystwyth Stroke Research Group, stroke.aber.ac.uk) including computer scientists, health and exercise physiologists, molecular biologists, psychologists, and neuroscientists. The project will be delivered in close collaboration with Prof Derek Jones and Dr Maryam Afzali from Cardiff University Brain Research Imaging Centre. In addition, the student will gain firsthand experience in interacting with real patients and health care professionals according to NHS ethical guidelines and data protection regulations.

We are looking for someone with analytical skills, and who is motivated with a strong passion for science. A good degree (2:1 minimum) in Computer Science or related fields, prior knowledge in data mining and modelling, and strong interest in human health and cognitive neuroscience is desired. 


References

Lugtmeijer, S., Lammers, N.A., de Haan, E.H.F., de Leeuw, F.-E., Kessels, R.P.C., 2021. Post-Stroke Working Memory Dysfunction: A Meta-Analysis and Systematic Review. Neuropsychology Review 31, 202-219.
van der Ham, I.J., Kant, N., Postma, A., Visser-Meily, J.M., 2013. Is navigation ability a problem in mild stroke patients? Insights from self-reported navigation measures. J Rehabil Med 45, 429-433.
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