PhD Studentship: Validating a Simple Measure for Assessing the Health of an Individual's Gait

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

The School of Sport, Rehabilitation and Exercise Sciences is offering a fully-funded studentship with an annual stipend of £15,609, plus Home tuition fees, for a maximum of 36 months.

The start date is October 2022

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.

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).

About you

Essential:

BSc (2:1 or above) in:

• Sports and Exercise Science;
• 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

About us

This project will be supervised by three experienced members of research staff from across two different schools; Dr Aleksandra Birn-Jeffery (lead supervisor) and Dr Matthew Taylor from The School of Sport, Rehabilitation & Exercise Sciences, and Dr Jackie Wong Siaw Tze from The Department of Mathematical Sciences. To have an informal discussion about the studentship, please contact Aleksandra on a.birn-jeffery@essex.ac.uk

How to apply

Application closing date: 18 July 2022

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

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