This research will investigate pervasive technologies that are able to support the accurate and objective assessment of symptoms known to be associated with Autism Spectrum Disorder (ASD). Within the UK and Ireland, the processes for diagnosing ASD are lengthy and compounded by significant waiting times for screening, which delay treatment and ultimately affect outcomes. One reason for such delays is the lack of a scientifically objective, reliable and valid assessment (Fombonne, 2018).
From a research and clinical perspective, the Mirror Neuron Dysfunction theory of ASD provides a robust basis for the development of objective Neuropsychological tests that are correlated with a diagnosis of ASD (Brighenti et al., 2018; Hamilton, 2013; Ramachandran et al., 2006). Research has suggested that Mirror Neurons support abilities such as empathy and the perception of another individuals intentions and as such presents an interesting focal point for assessment in ASD.
These assessments are traditionally conducted within clinical settings, using paper-based tools or basic computerised tests to capture data points surrounding attention, problem solving, memory, language, I.Q., visual-spatial skills, academic skills, and socio-emotional functioning (Lezak et al., 2012). Pervasive sensing technologies offer a novel perspective to this clinical domain. We postulate that neuropsychological deficits correlated with ASD can be more effectively assessed with passive sensor technologies, capable of measuring patterns of movement, gaze, behaviours, speech, facial expressions and responses to neuropsychological test stimuli, particularly when integrated with rich interactive assessment tools such as Serious Game environments (Jouen, A-L., 2017).
This project will seek to validate:
(a) the design of a serious game environment as a replacement for existing paper-based approaches to assessment;
(b) the capture and synchronisation of key metrics from both behavioural and physiological perspectives;
(c) the application of machine learning methods to explore patterns that may be present within data collected from case and control groups.
This research plans to ultilise a new collaboration with Psychology clinicians in the Republic of Ireland’s Health Service Executive (HSE) Child Development Team, based in County Cavan. The proposal fits with the University’s strategic theme of Healthy Communities and closely aligns with the Pervasive Computing Research Group, focusing upon research within the areas of Behaviour Analysis and Affective Computing. The project benefits from access to a range of existing pervasive and wearable sensing technologies and from a potential pilot site, in Cavan.
The supervisory team has expertise and experience in both the theory surrounding the work and its application to support people on the autism spectrum. Brighenti, et al. (2018). Neuropsychological aspects of Asperger Syndrome in adults: A review. Neuropsychological Trends, 63-95. Fombonne, E. (2018). Editorial: The rising prevalence of autism. Journal of Child Psychology and Psychiatry, 59(7), 717-720. doi:10.1111/jcpp.12941 Hamilton, A. (2013). Reflecting on the mirror neuron system in autism: A systematic review of current theories. Developmental Cognitive Neuroscience, 3, 91-105. doi:https://doi.org/10.1016/j.dcn.2012.09.008 Ramachandran, V., & Oberman, L. (2006). Broken Mirrors: A Theory of Autism. Scientific American, 295, 62-69. doi:10.1038/scientificamerican1106-62 Jouen, A-L et al. (2017) GOLIAH (Gaming Open Library for Intervention in Autism at Home): a 6-month single blind matched controlled exploratory study. Child and Adolescent Psychiatry and Mental Health, 11(17).
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
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:
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 studentship grant (RTSG) allocation to help support the PhD researcher.
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 studentship grant (RTSG) allocation to help support the PhD researcher.
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 studentship grant (RTSG) allocation to help support the PhD researcher.
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 studentship 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
As Senior Engineering Manager of Analytics at Seagate Technology I utilise the learning from my PhD ever day
Adrian Johnston - PhD in InformaticsWatch Video
Friday 7 February 2020
Late March 2020
The largest of Ulster's campuses
Monday 2 December 2019
Subject: Computer Science and Informatics
When applying for this PhD opportunity please quote reference number: