Transforming Pediatric Gait Analysis: Advancing Musculoskeletal Models for Children with Movement Disorders

Skeletal growth in children is a dynamic process influenced by biological and mechanical factors. While typically developing children experience predictable changes in bone structure, those with neuromuscular conditions such as cerebral palsy or developmental hip dysplasia face altered skeletal development. These conditions can lead to deformities affecting hip stability, gait, and limb alignment. Three-dimensional gait analysis is a critical tool for studying movement in such populations, enabling the development of targeted surgical interventions, orthoses, and rehabilitation plans. However, creating accurate pediatric musculoskeletal models is essential to fully leverage gait analysis data.

Existing approaches to model generation, such as scaling adult templates, fail to account for the unique geometry and physiology of children, who are not merely smaller versions of adults. Subject-specific models derived from medical imaging provide greater accuracy but are prohibitively time-consuming, requiring up to 12 hours per case. A promising alternative is statistical shape modeling, which scales template models based on a child’s anthropometry, offering a faster, more practical solution.

This project focuses on refining and expanding an existing articulated shape model of paediatric lower limb bones to improve predictions for children with movement disorders. Key objectives include testing these models on pathological populations, incorporating additional inputs including medical imaging data and torsional data, adding ankle bones for enhanced foot modeling, and comparing results with gold-standard imaging-based models.

By addressing the limitations of current methodologies, this project aims to create robust, clinically applicable models that enhance the accuracy of gait analysis and surgical planning for children with mobility challenges. The outcomes will empower healthcare professionals to deliver more effective and personalized care, making a meaningful difference in the lives of paediatric patients.

Students will work under the guidance of Dr. Laura Carman and Dr. Julie Choisne. The refined models will directly support clinicians in Australia and New Zealand gait labs as part of an Australia Medical Research Future Fund grant.

• Python coding experience
  

This project is eligible for funding but is subject to eligibility criteria & funding availability.

Page expires: 7th July 2025