Overview
During daily life activities we encounter different environments/challenges (e.g. walking to the shops), which are key to an individual’s quality-of-life.
When age, injury or disease creates movement difficulties, rapid health deterioration and loss in quality-of-life occur. Therefore, maintaining healthy gait across the population is vital for personal well-being and to reduce socioeconomic health burden.
The project
Reduced order models of locomotion describe walking and running, and are constructed using simple variables, (i.e. CoM) with known movement ranges. This makes them ideal for building multi-use models of gait health; adding Bayesian estimation and MCMC methodologies into the model allows incorporation of prior cycle knowledge that is necessary for modelling cyclical gait cycles.
Simple variables need to be validated for consistency across tasks (i.e. obstacle clearance), environments and applicability as simple screening tools. This validation could generate a ‘gait health’ assessment tool to assess intervention success, fall risk and track pathology progression.
The aims are:
- Determine how different methods of centre of mass extraction, across tasks, affect reduced order model parameters.
- Establish the applicability of a phone camera for measuring simple parameters to enable use across settings (2D vs 3D analysis).
- Build a model, leveraging the Department of Mathematical Sciences modelling expertise, defining gait health across anthropometric features (i.e. age [life span], gender, weight, and fitness).