PhD Study : AI-enabled Automated Behaviour Analysis for User-centric Systems

Summary

Computational Behaviour Analysis (CBA) is an emerging multidisciplinary research area which intends to develop computational models and methods to represent and analyse human behaviour. It achieves this by drawing expertise from computer science, human behaviour research and domain-dependent/application-specific studies such as healthcare, education and transport. The ultimate purpose of CBA is to quantitatively assess human behaviours automatically and objectively, identify long-term patterns and trends, thus recognising changes which can inform or enable advanced applications.

Such advanced applications may include but not limited to:

* automatic detection of the onset of medical conditions in healthcare.

* personalised teaching and learning in education.

* continuous assessment and adaptation of driving behaviour in intelligent transport.

CBA may incorporate quantitative assessment indicating how the quality of relevant behaviours are performed or quantification of changes within their performance. It is built upon, but advances beyond, activity modelling and recognition. In this context the capabilities of existing approaches for modelling human activities, i.e., data mining and machine learning based approaches, and domain and prior knowledge-based approaches, are rather limited.

For measuring and analysing human behaviour, CBA requires:

i.robust bootstrapping techniques for model estimation that draw from both domain related background knowledge and task-specific sample data at different levels of abstraction;

ii.adaptation techniques for data drifting and statistical behaviour modelling;

iii.model alignment techniques that produce metric-like scores for quality assessment;

iv.approaches for unsupervised modelling of “normal” behaviour and automatic detection of deviations from it. Nevertheless, CBA has so far received little attention as the research is still in its infancy.

This project will bridge the aforementioned knowledge gap by focusing on the following key aspects:

a)Free-style activity recognition – building upon our previous work in activity recognition towards recognition of non-predefined, randomly performed, activities by transferring raw sensor data into sequences of semantic events supported by semantic domain characterisation, and then processed by developing continuous progressive activity recognition methods.

b)Robust model estimation for CBA – by developing hybrid modelling approaches that integrate prior domain knowledge for rapid model bootstrapping, and investigating data-driven adaptation techniques to address data drifting and model evolution for personalized CBA. Modelling will focus on integrating hierarchical views on human behaviour at different levels of abstraction.

c)Continuous behaviour assessment – by pursuing quantitative analysis of human behaviour using numerical, metric-like scoring schemes for model alignment, which will also be used for unsupervised change detection in order to recognize abnormalities in sensor data streams.

For personalization the notion of “normality” will be learned automatically from unsupervised analysis of behaviour data. The proposed project is intended to make use of, but also contribute to, extensive expertise of data analytics, IoT, AI and smart environments within the School of Computing. It is aimed at developing generalizable techniques applicable to different use scenarios such as smart healthcare and AI-enabled education. It is expected that the project will generate high-value scientific outputs in top-tier journals and also provide inputs to research grant applications.

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.

• Sound understanding of subject area as evidenced by a comprehensive research proposal

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.

• Publications record appropriate to career stage
• A comprehensive and articulate personal statement
• Applicants will be shortlisted if they have an average of 75% or greater in a first (honours) degree (or a GPA of 8.75/10). For applicants with a first degree average in the range of 70% to 74% (GPA 3.3): If they are undertaking an Masters, then the average of their first degree marks and their Masters marks will be used for shortlisting.

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