Dr M Elliott, Prof T Arvanitis
Applications accepted all year round
About the Project
At the Institute of Digital Healthcare at WMG, University of Warwick, we are working to improve people’s health and wellbeing through the use of innovative digital technologies. As part of this we are developing and applying technologies that that will improve patient outcomes following physiotherapy and rehabilitation.
This PhD opportunity will focus on the use of machine learning and biomechanical modelling to identify compensatory movements. The project will involve:
- Investigating motion capture methods such as the use of inertial measurement units to measure movement trajectories
- Developing signal processing, algorithmic development and mathematical modelling methods to investigate deviations from expected movements.
- Investigating different patient groups and determine how the approach could be applied to improve physiotherapy and rehabilitation programmes.
The student will be expected to exploit IDH’s close collaborations with local NHS trusts to co-design a solution and collect data from a suitable sample of patients by the end of the project.
Background and need
Many musculoskeletal injuries and degenerative diseases (e.g. osteoarthritis) severely limit normal limb range of motion. This limited movement usually results from pain or muscle weakness and results in the individual making compensatory movements. These compensatory movements, whilst reducing pain or increasing function of the affected limb, can also cause abnormal loading on other parts of the body (e.g the unaffected limb) and increases risk of further injury. Importantly, it is often observed that even after surgery, patients continue to make the compensatory movements adopted prior to surgery, despite a substantial improvement in limb function, due to habit.
ENTRY REQUIREMENTS
- Applicants should have a 1st class or 2.1 degree in a relevant subject
- A relevant Master’s degree is desirable
- A strong background in Matlab/R or similar research programming languages and experience of analysing motion capture data is essential
- A good knowledge of vector mathematics and/or biomechanics would be beneficial
- Experience of working with clinical staff and/or working in an NHS environment is desirable
Funding Notes
FUNDING
This studentship is available to UK and EU students, according to fee status, who meet Research Council eligibility requirements based on residency. The studentship provides a tax free stipend of £14,296 per annum for three years, and all fees paid
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