This opportunity is now closed.

Funded PhD Opportunity

Automated Classification of Autism Spectrum Disorder in Children using Gait Analysis

Subject: Computer Science and Informatics


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

  • To hold, or expect to achieve by 15 August, an Upper Second Class Honours (2:1) Degree or equivalent from a UK institution (or overseas award deemed to be equivalent via UK NARIC) in a related or cognate field.
  • 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

    The University offers the following awards to support PhD study and applications are invited from UK, EU and overseas for the following levels of support:

    Vice Chancellors Research Studentship (VCRS)

    Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).

    This scholarship will cover full-time PhD tuition fees and provide the recipient with £15,000 maintenance grant 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.

    Vice-Chancellor’s Research Bursary (VCRB)

    Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).

    This scholarship will cover full-time PhD tuition fees and provide the recipient with £7,500 maintenance grant 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.

    Vice-Chancellor’s Research Fees Bursary (VCRFB)

    Fees only award (PhD fees + RTSG for 3 years).

    This scholarship will cover full-time PhD tuition fees 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.

    Department for the Economy (DFE)

    The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £ 15,009 per annum for three years. EU applicants will only be eligible for the fee’s component of the studentship (no maintenance award is provided). For Non-EU nationals the candidate must be "settled" in the UK. 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.

    Due consideration should be given to financing your studies; for further information on cost of living etc. please refer to: www.ulster.ac.uk/doctoralcollege/postgraduate-research/fees-and-funding/financing-your-studies


Other information


The Doctoral College at Ulster University


Reviews

As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day

Adrian Johnston - PhD in Informatics

Watch Video  

Key dates

Submission deadline
Monday 18 February 2019

Interview Date
19 - 20 March 2019


Applying

Apply Online  


Campus

Magee campus

Magee campus
A key player in the economy of the north west


Contact supervisor

Dr Pratheepan Yogarajah


Other supervisors

Related Funded Opportunities

Context aware Brain Computer Interfaces and Internet of Things (IoT)

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Autonomous Object Recognition for Robots

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Computer Science and Informatics

 View details

Neural Data Science, Computational Neuromodulation, and Metalearning

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Computer Science and Informatics

 View details

Augmented Reality Brain-computer Interface

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Computer Science and Informatics

 View details

Gaining a better understanding of our Planet through Deep Learning-based Data Analytics

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Data Analytic Technologies to Combat Human Trafficking

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Adaptive Learning for Modelling Non-stationary Dynamical Systems

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Computer Science and Informatics

 View details

Computational and Mathematical Modelling of Predator-Prey Systems

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Computing

Subject: Computer Science and Informatics

 View details

Applying Natural Language Processing to the automated fact checking of legal documents

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Computer Science and Informatics

 View details

Deep-learning assisted tele-medicine for the delivery of diabetic retinopathy screening in low- and middle-income countries

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Computer Science and Informatics

 View details

Intelligent Systems

Subject: Computer Science and Informatics

 View details

The impact of the analytical performance of laboratory tests on clinical decision making

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Computer Science and Informatics

 View details

Sensing human emotion within pervasive environments

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Magic Hands: Non-invasive hand tracking for virtual reality and game based stroke rehabilitation.

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Computer Science and Informatics

 View details

Intelligent Mobility Aids to Promote Physical Activity in Children with Cerebral Palsy

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Nursing and Health

 View details

Network Machine Learning Approach to Financial Crime Detection

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Trusting the uncertainty in machine learning predictions

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Mapping the Brain with Zero Knowledge using advanced AI and Data Analytics

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Computer Science and Informatics

 View details

Neuro Sense: serious games for in-situ autism assessment

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Exploiting Brain Inspired Information Processing in Hardware to Develop Highly Reliable, Always-on Smart Sensor Systems.

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Computer Science and Informatics

 View details

Autonomous Decision Analytics by Integrating Machine Learning and Symbolic Reasoning

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

An Approach for Constructing and Sharing Open Data Sets in Experimental Pervasive Computing

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

RECASE: Robot-Enabled Care system in Smart Environments

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Sensing and Modelling Air Quality for Healthy Living (SMAQ)

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Chatbots for decision support and reporting in healthcare

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

PostCrypt: Data Security for the future

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Enhanced Augmented Reality with Data Engineering and AI for Smart Digital Education

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics|Education

 View details

AI-enabled Automated Behaviour Analysis for User-centric Systems

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Quantitatively assessment of limb motion utilising wearable sensors in remote rehabilitation

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details

Towards Trusted Cognitive Intelligence for User-centric Smart Systems

Closing date:
Friday 7 February 2020
Subject: Computer Science and Informatics

 View details