PhD Study : Automated Classification of Autism Spectrum Disorder in Children using Gait Analysis

Summary

Autism spectrum disorder (ASD) is a lifelong neurodevelopmental disorder that can be recognised at an early age, typically before the age of three. It is a spectrum of pervasive developmental disorders and is found across all ethnic cultures and economic groups.  ASD is a condition that can be characterised by a constant deficit in social communication, social interaction and the presence of restrictive and repetitive behaviour. Children with ASD often demonstrate restrictive and repetitive behaviour known as motor stereotypies, which can be defined as “involuntary, coordinated, patterned, repetitive, rhythmic, and purposeless but seemingly purposeful movements” [1].

These stereotypic behaviours are as a result of difficulty with motor function and coordination, and often identified by abnormal gait, clumsiness and irregular motor signs [2]. Abnormal gait is defined as an unusual style of walking from the normal walking pattern and this could cause deterioration in occupational and other substantial ranges of daily activities.

Children with ASD tend to augment their walking stability with a reduced stride length, increased step width and therefore wider base of support, and increased time in the stance phase. A number of studies have addressed various types of gait disturbance in children with ASD [1-3]. In many Low and Middle-Income Countries (LMIC), there is a shortage of Mental Health Specialists (MHS) to conduct ASD screening. This results in long wait times (years) for children, with many children never getting an opportunity to see a specialist at all. This challenge could be overcome if there could be an automated mechanism for conducting this screening. Automated classification of ASD gait could provide assistance in diagnosis and ensure rapid quantitative clinical judgment.

The use of machine learning classifiers for automated recognition of gait pattern deviations has grown enormously in the last decade [4-6]. However, the published literature focusing on automated classification on ASD gait patterns is still scarce. Even though the interest in gait analysis is becoming popular among researchers, very few quantitative studies have been conducted on children with autism. Thus, this project will harness this opportunity by proposing a biologically inspired approach for the automated classification of gait patterns of children with ASD based on video sequences using spatial-temporal parameters.

The project will focus on producing a low-cost solution, using technology such as mobile phones to allow parents in LMIC to capture video sequences that can be used for automated screening. This project is multidisciplinary in nature and will leverage existing research links with Dr Sudarshi Seneviratne, Lecturer in Child and Adolescent Psychiatry, University of Colombo, Sri Lanka, to provide a solution for early detection of ASD using an appropriate set of gait features along with biologically inspired machine learning approaches.

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• Experience using research methods or other approaches relevant to the subject domain

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 70%
• For VCRS Awards, Masters at 75%
• Publications - peer-reviewed

Funding and eligibility

The University offers the following levels of support:

Vice Chancellors Research Studentship (VCRS)

The following scholarship options are available to applicants worldwide:

• Full Award: (full-time tuition fees + £19,000 (tbc))
• Part Award: (full-time tuition fees + £9,500)
• Fees Only Award: (full-time tuition fees)

These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

• Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
• Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
• Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
• Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Due consideration should be given to financing your studies. Further information on cost of living