Investigating machine learning and biomechanical modelling approaches to identify compensatory movements

The project 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
project alligns with epsrc research activities around Healthcare technologies, Assistive technology, rehabilitation and musculoskeletal biomechanics