EPSRC DTP PhD studentship: Developing gaze training for skilled upper-limb prosthetic use
University of Exeter - College of Engineering, Mathematics and Physical Sciences
|Funding for:||UK Students, EU Students|
|Funding amount:||£14,296 per annum|
|Placed on:||1st November 2016|
|Closes:||11th January 2017|
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Main supervisor: Prof Krasimira Tsaneva-Atanasova (University of Exeter)
Co-supervisor: Dr Gavin Buckingham (University of Exeter)
Co-supervisor: Dr Sam Vine (University of Exeter)
Co-supervisor: Dr Greg Wood (Manchester Metropolitan University)
Co-supervisor: Dr Mark Wilson (University of Exeter)
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.
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South West England