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Markerless Tracking for the Clinical Analysis of Patients with Symptomatic Gait

PGR-P-693

Key facts

Type of research degree
PhD
Application deadline
Friday 17 April 2020
Project start date
Thursday 1 October 2020
Country eligibility
UK and EU
Funding
Competition funded
Source of funding
Research council
Supervisors
Dr Todd Stewart and Dr He Wang
Additional supervisors
Professor Anthony Redmond (School of Medicine)
<h2 class="heading hide-accessible">Summary</h2>

Clinical gait analysis has several limitations including high BMI (skin artefact), patient sensitivity, co-morbidity (other health issues), and importantly the time it takes to apply markers to the patient. Recent advancements in gaming technologies that involve markerless tracking may have potential to improve efficiencies in the clinical setting. The student will utilise advancements in machine learning towards investigating markerless tracking in a clinical setting.

<h2 class="heading hide-accessible">Full description</h2>

<p>Biotribology involves the analysis of motion and load to optimise friction, lubrication and wear in natural and artificial joints. The complex movement patterns that occur locally between the joint surfaces can be analysed using gait analysis with subsequent multibody dynamic modelling of joint reaction forces. This data can then be used for the design of joint replacements or other tissue engineered solutions that enable movement to be returned to the patient following joint disease. For these solutions to work effectively the patient requires a dynamic movement pattern that encourages improved lubrication mechanisms. Presently, this is limited by the ability to collect large numbers of patient movement data under a broad range of activities.</p> <p>Pose reconstruction or tracking has been widely studied in computer vision. It aims to recover the skeletal motions of people in videos captured in different settings, e.g. monocular vs multi-view camera, controlled environment vs in-the-wild. The research in this field has been driven by data-driven approaches such as traditional machine learning (statistical or others) and cutting-edge deep learning. Its applications include action recognition for surveillance purposes, autonomous vehicles on pedestrian behavioural prediction, action sensing in Kinect for games, etc. This line of research is superior in term of its physical non-invasiveness, as oppose to other motion tracking methods, but inferior in terms of accuracy, being subject to occlusions, lighting and other conditions.</p> <p>The potential exists to utilise markerless tracking to open up gait analysis to a broader clinical spectrum of patients thus enabling more patients to benefit from improved joint therapies.&nbsp;This project aims to, based on existing pose reconstruction research, propose new reliable, accurate and non-invasive way (e.g. camera-based) to accurately reconstruct poses and motions, with the main purpose of serving medical diagnosis for injuries and potential pathological motion abnormalities.&nbsp;The project will lead to direct benefits to the patient and the healthcare system.</p>

<h2 class="heading">How to apply</h2>

<p>Formal applications for research degree study should be made online through the&nbsp;<a href="https://eps.leeds.ac.uk/mechanical-engineering-research-degrees/doc/apply">University&#39;s website</a>. Please state clearly in the research information section&nbsp;that the research degree you wish to be considered for is &ldquo;Markerless Tracking for the Clinical Analysis of Patients with Symptomatic Gait&rdquo;&nbsp;as well as&nbsp;<a href="https://eps.leeds.ac.uk/mechanical-engineering/staff/165/dr-todd-stewart">Dr Todd Stewart</a>&nbsp;as your proposed supervisor.</p> <p>If English is not your first language, you must provide evidence that you meet the University&#39;s minimum English language requirements (below).</p> <p><em>We welcome applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.</em></p>

<h2 class="heading heading--sm">Entry requirements</h2>

Applicants to research degree programmes should normally have at least a first class or an upper second class British Bachelors Honours degree (or equivalent) in an appropriate discipline. The criteria for entry for some research degrees may be higher, for example, several faculties, also require a Masters degree. Applicants are advised to check with the relevant School prior to making an application. Applicants who are uncertain about the requirements for a particular research degree are advised to contact the School or Graduate School prior to making an application.

<h2 class="heading heading--sm">English language requirements</h2>

The minimum English language entry requirement for research postgraduate research study is an IELTS of 6.0 overall with at least 5.5 in each component (reading, writing, listening and speaking) or equivalent. The test must be dated within two years of the start date of the course in order to be valid.

<h2 class="heading">Funding on offer</h2>

<p>UK/EU &ndash; Engineering &amp; Physical Sciences Research Council Studentships paying academic fees of &pound;4,600 for Session 2020/21, together with a maintenance grant of&nbsp;&pound;15,009 for&nbsp;Session 2020/21 paid at standard Research Council rates for 3.5 years. UK applicants will be eligible for a full award paying tuition fees and maintenance. European Union applicants will be eligible for an award paying tuition fees only, except in exceptional circumstances, or where residency has been established for more than 3 years prior to the start of the course. Funding is awarded on a competitive basis.</p>

<h2 class="heading">Contact details</h2>

<p>For further information regarding your application, please contact Doctoral College Admissions: e:&nbsp;<a href="mailto:phd@engineering.leeds.ac.uk">phd@engineering.leeds.ac.uk</a>, t: +44 (0)113 343 5057.</p> <p>For further information regarding the project, please contact Dr Todd Stewart:&nbsp; e: <a href="mailto:t.d.stewart@leeds.ac.uk">t.d.stewart@leeds.ac.uk</a></p> <p>&nbsp;</p>


<h3 class="heading heading--sm">Linked funding opportunities</h3>