Validating a simple measure for assessing the health of an individual’s gait

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:

1. Determine how different methods of centre of mass extraction, across tasks, affect reduced order model parameters.
2. Establish the applicability of a phone camera for measuring simple parameters to enable use across settings (2D vs 3D analysis).
3. 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).

Funding

The award consists of full Home tuition fees  (further fee details), and an annual doctoral stipend of £16,062, for a maximum of 36 months.

PhD students also receive a £2,500 training bursary via Proficio funding, which may be used to cover the cost of advanced skills training including conference attendance and travel.

Criteria

Essential

• BSc (2:1 or above) in: Sports and Exercise Science; or a Biomechanics / Mathematics / Engineering / Computational statistics related subject.

Desirable

• MSc/MRes in any of the above subject areas
• Proficiency in programming such as R, Python, MatLab

How to apply

You can apply for this postgraduate research opportunity online.

Applications MUST include the following documents to be uploaded:

• Research proposal of no more than 600 words, outlining suggested research focus(es), approach(es) and/or studies that applicants would be interested to include within their PhD.
• CV 
• Covering letter
• Transcripts of any undergraduate or masters programmes. 

Instruction to applicants

When you apply online you will be prompted to fill out several boxes in the form:

• For "Course title" please put "PhD Health and Physical Activity"
• For "Proposed research topic or area of research" please put the title of this studentship (Validating a simple measure for assessing the health of an individual’s gait)
• For "If you have contacted a potential supervisor..." please put the name of the lead supervisor (Dr Aleksandra Birn-Jeffery)

If you have any informal queries about this opportunity please email the lead supervisor, Dr Aleksandra Birn-Jeffery (a.birn-jeffery@essex.ac.uk).

You can find the terms and conditions of this studentship here (PDF).