[Comp-neuro] PhD positions at the University of Exeter: Developing gaze training for skilled upper-limb prosthetic use Ref: 2407

Tsaneva-Atanasova, Krasimira K.Tsaneva-Atanasova at exeter.ac.uk
Sat Dec 17 13:32:18 CET 2016


About the award
This project is one of a number which are funded by the Engineering and Physical Sciences Research Council (EPSRC) Doctoral Training Partnership to commence in September 2017. This project is part of a competition for funding. Usually the project which receives the best applicant will be awarded the funding. The studentships will provide funding for a stipend which, is currently £14,296 per annum for 2016-2017, research costs and UK/EU tuition fees at Research Council UK rates for 42 months (3.5 years) for full-time students, pro rata for part-time students.
Please note that of the 56 projects advertised we expect that up to 20 posts will be filled.
Location:   University of Exeter, Streatham Campus, Exeter, Devon
Supervisors:
Main supervisor: Prof Krasimira Tsaneva-Atanasova<http://emps.exeter.ac.uk/mathematics/staff/kt298> (University of Exeter)
Co-supervisor:  Dr Gavin Buckingham<http://sshs.exeter.ac.uk/staff/index.php?web_id=Gavin_Buckingham> (University of Exeter)
Co-supervisor:  Dr Sam Vine<http://sshs.exeter.ac.uk/staff/index.php?web_id=Samuel_Vine> (University of Exeter)
Co-supervisor:  Dr Greg Wood<http://www.cheshire.mmu.ac.uk/exspsci/ourstaff/profile/index.php?profile_id=2576> (Manchester Metropolitan University)
Co-supervisor:  Dr Mark Wilson<http://sshs.exeter.ac.uk/staff/index.php?web_id=Mark_Wilson> (University of Exeter)
Project Description:
Learning to use a prosthetic limb is inherently difficult and requires a huge amount of concentration. Like learning how to wield a new tool for the first time, amputees need to acquire the confidence and dexterity required for skilled action. In order to produce accurate goal-directed movements the motor system requires accurate and timely visual information making the timing and location of the person’s gaze, with relation to the movement of their limbs, critical for skilled behaviour. There is, however, no structured training protocol for use with prosthetic hands. We aim to develop a novel gaze training regime to facilitate the use of a prosthetic hand to skilfully interact with objects.
In the first part of the project, the student will undertake an observational study to determine what factors lead some individuals to become skilled with a prosthesis faster than others. We will test large numbers of intact (i.e., without amputation) participants learning to use a state-of-the-art myoelectric prosthetic arm simulator, which is controlled by muscle feedback but ergonomically designed to fit over the wrist of an intact hand. Participants will move objects of different of sizes and weights from one location to another with a range of precision requirements and in the presence of a range of obstacles. Over multiple sessions we will measure hand and object kinematics, fingertip forces, and eye path with a head-mounted eye tracker. We will then develop a data-driven mathematical model of the eye tracking and biomechanical performance data. Statistical analysis of the patterns of eye movement will provide new insights into the ‘signature’ of good performance using the prosthetic arm.
The second phase will use the data from Project 1 to develop a training protocol that will adopt the ‘expert signatures’ from project work 1 as a prototype for a trainee to follow. We will focus predominantly on the signature of expertise derived from the gaze behaviour measures and implement a gaze training protocol. We will then test the efficacy of this novel training with new set of intact participants using the prosthetic simulator, tracking their performance in comparison to individuals who will receive a sham training protocol.
In the final stage of the project work, the gaze training protocol will be used in a sample of upper-limb-amputees as they learn how to use their new prosthesis. As this state of the project will not be limited to myoelectric prosthetic users, this will also allow us to test the generalizability of our training protocol. The students will be involved in these experiments by running model simulations and data analysis to provide quantitative information for the training protocol as well as focussing on completing the write-up of their PhD thesis.
Entry requirements:
Applicants should have obtained, or be about to obtain, a First or Upper Second Class UK Honours degree, or the equivalent qualifications gained outside the UK.   Applicants with a Lower Second Class degree will be considered if they also have Master’s degree.  Applicants with a minimum of Upper Second Class degree and significant relevant non-academic experience are encouraged to apply.  All applicants would need to meet our English language requirements by the start of the  project http://www.exeter.ac.uk/postgraduate/apply/english/.
The majority of the studentships are available for applicants who are ordinarily resident in the UK and are classed as UK/EU for tuition fee purposes.  If you have not resided in the UK for at least 3 years prior to the start of the studentship, you are not eligible for a maintenance allowance so you would need an alternative source of funding for living costs.To be eligible for fees only funding you must be ordinarily resident in a member state of the EU
Applicants who are classed as International for tuition fee purposes are not eligible for funding.
Summary
Application deadline:   11th January 2017
Number of awards:       1
Value:  3.5 year studentship: UK/EU tuition fees and an annual maintenance allowance at current Research Council rate. Current rate of £14,296 per year
Duration of award:      per year
Contact: Doctoral College       Doctoral.College at exeter.ac.uk<mailto:Doctoral.College at exeter.ac.uk>
How to apply
Click here to apply<http://www.exeter.ac.uk/postgraduate/money/funding/application/>
Please be aware you will be asked to upload the following documents:
•       CV
•       Letter of application outlining your academic interests, prior research experience and reasons for wishing to undertake the project.
•       Transcript(s) giving full details of subjects studied and grades/marks obtained.  This should be an interim transcript if you are still studying.
•       If you are not a national of a majority English-speaking country you will need to submit evidence of your current proficiency in English.  For further details of the University’s English language requirements please see http://www.exeter.ac.uk/postgraduate/apply/english/.



The closing date for applications is midnight on 11 January 2017.  Interviews will be held at the University of Exeter between 13 February and 17 February 2017.



If you have any general enquiries about the application process please email Doctoral.College at exeter.ac.uk<mailto:Doctoral.College at exeter.ac.uk>
or phone +44 (0)1392 722311.  Project-specific queries should be directed to the supervisor.



During the application process, the University may need to make certain disclosures of your personal data to third parties to be able to administer your application, carry out interviews and select candidates.  These are not limited to, but may include disclosures to:
• the selection panel and/or management board or equivalent of the relevant programme, which is likely to include staff from one or more other HEIs;
• administrative staff at one or more other HEIs participating in the relevant programme.
Such disclosures will always be kept to the minimum amount of personal data required for the specific purpose. Your sensitive personal data (relating to disability and race/ethnicity) will not be disclosed without your explicit consent.


Krasimira Tsaneva-Atanasova
Professor of Mathematics for Healthcare
Department of Mathematics
College of Engineering, Mathematics and Physical Sciences
University of Exeter
Exeter, Devon, EX4 4QF, UK
tel: +44 (0) 1392 723615
email: k.tsaneva-atanasova at exeter.ac.uk<mailto:k.tsaneva-atanasova at exeter.ac.uk>
web: http://emps.exeter.ac.uk/mathematics/staff/kt298

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