Machine Learning for Gait Assessment in Children with Cerebral Palsy

Overview

Cerebral Palsy (CP) is a non-progressive group of movement disorders with many sub-types and a broad range of symptoms that varies between individuals. Though the disorder is non-progressive, clinical symptoms may change throughout development and the life of a child. Early intervention can improve outcomes in children with CP.  This project will develop and evaluate machine learning methods for gait assessment in children with CP and investigate how this can be applied for monitoring clinical interventions for treatment of CP. This interdisciplinary project combines biomechanics, machine learning, and healthcare.

Research engagement

Students will undertake a structured research process including a targeted literature review on gait assessment and cerebral palsy, followed by development of machine learning models using biomechanical data. Activities include data preprocessing, feature extraction, model implementation, and performance evaluation. Results will be interpreted in a clinical context, with regular guidance supporting interdisciplinary understanding and application.

Research activities

The project will involve evaluating existing gait assessment and machine learning approaches, followed by the development of context-aware models tailored to biomechanical data from children with cerebral palsy. The student will collaborate with researchers in biomechanics and artificial intelligence to ensure methods are both technically robust and clinically relevant. Model performance will be systematically assessed and interpreted in the context of supporting clinical decision-making.

Key activities include:

• Review and critical analysis of current literature and methods
• Preprocessing and exploration of gait and biomechanical datasets
• Design and implementation of machine learning models
• Evaluation and validation of model performance
• Interpretation of results with clinical relevance
• Collaboration with interdisciplinary research teams

Research skills

You will gain experience in engineering research and development, and how to translate this research to address real-world problems, particularly for clinical applications. You will develop skills needed for research, such has how to thoroughly evaluate existing research, identify important gaps, and how to develop solutions to these problems. All of this will include experience with software development and testing, feature engineering, and machine learning.

Outcomes

Expected outcomes include the development and validation of an improved machine learning methods for gait assessment in children with Cerebral Palsy. Supervisory team will aid in direction of development for these outcomes.

Skills and experience

A strong foundation in Python programming is required, as well as a deep interest in research and development. It is desirable for a suitable candidate to have prior exposure to the following areas (though not required and these skills will be developed over the research project) :

• Machine Learning concepts
• Software Version Control and Software Engineering best practices

Start date

2 November, 2026

End date

19 February, 2027

Location

QUT Gardens Point Campus

Keywords

• AI for Healthcare
• Machine Learning

Contact

Contact the supervisor for more information.