PhD Studentship: Investigating Machine Learning and Biomechanical Modelling Approaches to Identify Compensatory Movements

University of Warwick - WMG

Funding: Funded for 3 years for Home/EU students

Project Supervisors: Dr Mark Elliott, and Professor Theo Arvanitis

Start date: As soon as possible

Project aims

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:

  • 1st class/2.1 degree in a relevant subject, e.g. engineering, mathematics or computer science
  • Relevant Masters level degree (desirable)
  • Strong background in Matlab/R or similar programming languages
  • Interest in machine learning and/or maths modelling


This studentship is available to Home 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, and all fees paid, for three years.

To apply:

This is a COMPETITIVE application process and a formal application must be completed. The information supplied will then be sent for review to assess your suitability and interviews will be conducted.

As part of your application you should provide a 1-page statement detailing your research background to date and how your expertise matches the requirements of this project.

To submit your application, please complete our online enquiry form

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Midlands of England