Technology to advance treatment of children with movement disorders
Masters Project
Our vision is to use computational models informed by wearable sensors to interpret movement and provide clinicians with information about an individual patient’s anatomy, biomechanics and motor control. These data can then be used with Machine Learning classification schemes to provide clinicians with personalized treatment options. This data-driven approach has the potential to transform the rehabilitation of movement disorders. The aim of this project is to understand how bone shape and muscles parameters change with growth to create personalized models of children.
To achieve this aim, the project will be divided into 2 objectives:
- Develop population based computational models using Principal Component Analysis (PCA) and Partial Least Square Regression techniques (PLSR)
- Validate the models’ accuracy on post processed MRI and motion capture data from typically developed children and children with cerebral palsy
The candidate will be working with already collected and processed medical images and motion capture data of typically developed children and children with Cerebral Palsy participants. The tools and code to create the personalized computational models are freely available.
The outcome of this model will be used by our clinical partners working in clinical gait analysis across Australia and New Zealand to improve diagnosis and treatment plan in children with movement disorders.
Contact and supervisors
Contact/Main supervisor
Supporting supervisor(s)
- Professor Thor Besier
- Dr Laura Carman
Page expires: 2 September 2024