Using digital twins and synthetic data for the development of activity recognition models

Summary

​One of the main challenges in the development of data driven approaches to activity recognition is the availability of high quality and labelled datasets.  Whilst this has been a historical issue within the field, it has been further compounded over the last 30 months with data collection studies within smart environments involving human participants being limited due to the COVID-19 pandemic.  One potential solution to this challenge is to leverage the paradigm of the Digital Twin and usage of synthetic data generation.  Whilst this goes somewhat towards solving the problem, the generalization of synthetic data can be compromised due to its inability to fully represent the range of variation in human activity.

It is the aim of this Project to develop approaches to produce synthetic data in an effort to address the challenges associated with the lack of high quality and annotated datasets within the field of activity recognition.  The synthetic data produced will be studied within the context of real data and where necessary transformation processes will be introduced to ensure it is representative of the real data. In addition the digital twin paradigm will be used in an effort to improve the performance of data driven approaches to activity recognition.  By using this process the effects of synthetic data will be considered and how it may impact on the overall activity recognition performance.

The following research questions will be considered:

-can synthetic data be generated which is representative of the real data within the context of activity recognition?

-can a model be developed to generate synthetic data which extends upon the generalisation of the original data available?

-what are the effects of using a mix of both synthetic and real data on the performance of activity recognition models?

-can the digital twin paradigm be used to assist in improving the performance of data driven activity recognition models?

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.

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%
  • Experience using research methods or other approaches relevant to the subject domain
  • Work experience relevant to the proposed project
  • Publications - peer-reviewed

Funding and eligibility

The University offers 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 £18,000 (tbc) 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.

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.

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.

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 £8,000 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.

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.

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.

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.

Department for the Economy (DFE)

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £18,000 (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

Recommended reading

Cruciani, F., Nugent, C.D., Quero, J.M., Cleland, I., McCullagh, P., Synnes, K., Hallberg, J., Personalizing activity recognition with a clustering based semi-population approach, 2020, IEEE Access, 8, 9258905, , 10.1109/ACCESS.2020.3038084.

M Ortiz-Barrios,  E Jarpe,  M Garcia,  I Cleland,  CD Nugent,  S Arias-Fonseca,  N Jaramillo-Rueda,  Predicting activity duration in smart sensing environments using synthetic data and partial least squares regression:  the case of dementia patients,  Sensors,  vol. 22,  no. 5410,  2022.

Miguel Angel Ortíz-Barrios, I Cleland, CD Nugent, P Pancardo, E Järpe, J Synnott,  Using simulated data for estimating real sensor events: A Poisson-regression-based modelling,  Remote Sensing,  vol. 12,  no. 5,  771,  2020.

LH Yang,  L Martinez,  J Liu,  CD Nugent,  Online Updating Extended Belief Rule-Based System for Sensor-Based Activity Recognition,  Expert Systems With Applications,  vol. 186,  2021.

A Polo-Rodriguez,  J Medina,  F Cruciani,  CD Nugent,  Domain adaptation of binary sensors in smart environments by means of activity alignment,  IEEE Access,  vol. 8,  pp. 228804-228817,  2020.

The Doctoral College at Ulster University

Key dates

Submission deadline
Monday 27 February 2023
04:00PM

Interview Date
week commencing 17 April 2023

Preferred student start date
18 Sept 2023

Applying

Apply Online  

Contact supervisor

Professor Christopher Nugent

Other supervisors

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