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Characterising coordination difficulties in autism using computational techniques


Project Description

Autism is a life-long developmental condition that affects how a person communicates and interacts with people. ~80% of autistic individuals also have altered motor control such as less accurate eye-hand coordination and abnormal gait patterns, causing considerable problems with activities of daily living. Despite this impact, coordination difficulties are poorly characterised as current assessment techniques do not provide quantitative data on the spatial and temporal nature of movements. This problem prevents advances in understanding the aetiology of coordination difficulties or whether they could be used to diagnose autism.

Objective: To apply computational and statistical methods to motion tracking data in order to determine the nature of coordination difficulties in autistic adults and whether they can be used in diagnosis. Our preliminary work demonstrates the potential of this approach achieving 87% classification accuracy on a dataset of 30 autistic and non-autistic people.

Methods: Autistic adults will perform a number of different actions (e.g. balancing, pointing), while motion sensors track their movements. Different movement parameters will be extracted (e.g. velocity, amplitude, joint angles) and computational and statistical methods (e.g. feature extraction, machine learning) will be applied to discriminate autistic and non- autistic groups

Impact: The project has the potential to produce a prototype tool to identify those at risk from having autism as well as detecting and classifying motor impairments. This is important as autistic adults have placed the need for earlier and improved diagnosis in their top 10 research priorities.

The student will join a vibrant research team in the Body, Eye and Movement (BEAM) lab4 and Alex Cassons lab which links data from sensor devices to Machine Learning analyses.5 There are regular opportunities to become involved in activities such as teaching and public engagement and the student will benefit from the interdisciplinary research network [email protected] (http://www.autism.manchester.ac.uk/), chaired by Emma Gowen.

Candidates are expected to hold (or be about to obtain) a minimum 2:1 (or equivalent) in a related area / 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/). Informal enquiries may be made directly to the primary supervisor.

For international students we also offer a unique 4 year PhD programme that gives you the opportunity to undertake an accredited Teaching Certificate whilst carrying out an independent research project across a range of biological, medical and health sciences. For more information please visit http://www.internationalphd.manchester.ac.uk

Funding Notes

This project has a Band 1 fee. Details of our different fee bands can be found on our website (View Website). For information on how to apply for this project, please visit the Faculty of Biology, Medicine and Health Doctoral Academy website (View Website).

Informal enquiries may be made directly to the primary supervisor.

References

1. Gowen E and Hamilton A (2013) Motor abilities in autism: a review using a computational approach. J Autism Dev Disord. 43(2) 323-344
2. Li, B., Meng, J., Sharma, A., Purushwalkam, S. & Gowen, E (2017) Applying Machine Learning to Identify Autistic Adults Using Imitation: An Exploratory Study PLoS ONE. 12, 8, e182652.
3. https://www.autistica.org.uk/our-research/our-research/your-research-priorities
4. http://beamlab.lab.ls.manchester.ac.uk/
5. https://www.research.manchester.ac.uk/portal/alex.casson.html

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