Automatic Stroke Recovery Prediction using Artificial Intelligence

Lead Research Organisation: Aberystwyth University
Department Name: Computer Science


Stroke is a leading cause of adult disability across UK and worldwide. People with stroke have reduced mobility, and because of sedentary lifestyle, they are more likely to develop other health issues including cardiovascular problems, dementia and depression. Previous studies have shown that exercise can lead to improvements in balance, walking and aerobic fitness of stroke survivors. However so far, we have limited understanding of neural, physiological and molecular mechanisms of exercise-induced stroke recovery. To elucidate some of these mechanisms, we have recently started an NHS-funded stroke rehabilitation project. Chronic stroke survivors participate in a longitudinal study focused on intensive-exercise rehabilitation with minimum duration of three months. During the study, we continuously monitor the progress of subjects by collecting a wide range of data from motion capture system, wearable sensors, omics technologies, physiological and cognitive tests, observer rated clinical measurements, medical records, patient interviews and surveys. Over time, the project will produce a rich dataset in various formats and at different temporal resolutions. The PhD student will contribute to the project by developing new rule-based, probabilistic and statistical machine learning approaches to integrate and analyse the multimodal, multifaceted data (e.g. fuzzy logic, Bayesian networks and convolutional neural networks). The overall goal is to design an AI-based recommendation system to assist health care Professionals in monitoring stroke recovery and predicting its outcome. This is a challenging AI problem, and the student will be trained in cluster computing, machine learning and big data approaches as part of their PhD program. They will also have the opportunity to apply their knowledge to other domains dealing with complex and multimodal data sets. The student will be part of a vibrant, multi-disciplinary research team including computer scientists, physicians, exercise rehabilitation experts, molecular biologists, psychologists and neuroscientists. In addition, they will gain first-hand experience in interacting with real patients according NHS ethical guidelines and data protection regulations.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S023992/1 01/04/2019 30/09/2027
2431345 Studentship EP/S023992/1 01/10/2020 30/09/2024 Bishnu Paudel