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(EPSRC DTP) Combining computational techniques with movement data to predict adult autism diagnosis


Project Description

Background Autism is a life-long developmental condition that affects how a person communicates and interacts with people. In addition to these social aspects, ~80% of autistic individuals have coordination difficulties such as poor eye-hand coordination, unstable balance and unusual gait. The healthcare aim of this project is to uncover whether these coordination difficulties can be used to diagnose autistic adults.

Currently, diagnosis of autistic adults is difficult and time consuming and autistic adults have placed the need for earlier and improved diagnosis in their top 10 research priorities. This is because existing diagnostic criteria have not been validated in an adult population, autistic adults have developed compensatory strategies and the subjective nature of the observational inventories mean that diagnosis can vary between clinicians. Consequently, access to valuable support is delayed. This project will combine motion tracking data collected during movement tasks with data science methods to investigate whether an automated test based on coordination skills could provide added value for diagnostic precision, when used in combination with current observational inventories. Using movement tasks to diagnose adults is advantageous over current methods as coordination difficulties occur throughout the lifespan and movement can be measured quantitatively and objectively, providing a rich dataset to identify discriminating features.

Our recent EPSRC-funded work demonstrates the potential of this approach. Machine Learning (ML) techniques were applied to motion tracking data from 44 autistic and non-autistic participants while they made aiming movements or imitated actions achieving 71% and 75% classification accuracy respectively. We have shown that previous studies using ML to classify autism report an over inflated accuracy of >80% due to using the same data to train and test models. Importantly, we are confident that our results are not over inflated as we developed novel, robust methods to avoid data over-fitting. We now need to create new models on a wider repertoire of movements and larger sample size to enable identification of consistent motor patterns that will increase classification accuracy.

Applications are invited from UK/EU nationals only. Applicants must have obtained, or be about to obtain, at least an upper second class honours degree (or equivalent) in a relevant subject.

For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (https://www.bmh.manchester.ac.uk/study/research/apply/)

Funding Notes

EPSRC DTP studentship with funding for a duration of 3.5 years to commence in September 2020. The studentship covers UK/EU tuition fees and an annual minimum stipend £15,285 per annum. Due to funding restrictions, the studentship is open to UK and EU nationals with 3 years residency in the UK.

As an equal opportunities institution we welcome applicants from all sections of the community regardless of gender, ethnicity, disability, sexual orientation and transgender status. All appointments are made on merit.

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