Process Mining and Modelling for sensor-based patient activity recognition in a Smart Home

Summary

Processes are often made up of a series of activities that are time-stamped, and recent work in process mining and event-data modelling has mostly been carried out to gain new knowledge and measure how efficiently a process is executed. Like business processes for modern organisations and industry processes, sensor networks can be thought of as a way to keep a watch on an executing process, with sensor activations demarking the transitions between stages.

The use of health monitoring systems makes it easier to take care of long-term illnesses and can improve patient safety and help doctors better diagnose and treat patients, and they can also be used to keep an eye on long-term conditions from remote sensing. A network of sensors installed in a Smart Home can collect data on a patient's activities and provide assistance when necessary. The sensor network is comprised of a wide variety of electronic components, including but not limited to accelerometers, heart monitors, smartphones, intelligent household appliances, and cameras. At the same time that the sensor activities are taking place, the patients and residents participate in linked subsequences of activities. The fulfilment of a need, or goal, such as “taking pills” or “making a cup of tea”, is the purpose of each of these operations. However, it is not uncommon for issues or anomalies to crop up in the processes that will call for human intervention.

This PhD focuses on the use of process mining for creating probability models for recognising patients' activities of daily living in the context of smart homes. This will involve use of Markov Chains to describe associations for sensor groups that form clusters within the overall process. Such knowledge will facilitate accurate predictions of patient activities, based on knowledge of patient activities, which has been extracted from the connected sensors.

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

Tariq, Z., Khan, N., Charles, D., McClean, S., McChesney, I. and Taylor, P., 2020. Understanding contrail business processes through hierarchical clustering: A multi-stage framework. Algorithms, 13(10), p.244.

Tariq, Z., Charles, D., McClean, S., McChesney, I. and Taylor, P., 2021, August. An Event-Level Clustering Framework for Process Mining Using Common Sequential Rules. In International Conference for Emerging Technologies in Computing (pp. 147-160).

Springer, Cham. Shuai Zhang, Sally I. McClean, Bryan W. Scotney (2012): Probabilistic Learning From Incomplete Data for Recognition of Activities of Daily Living in Smart Homes, IEEE Transactions on Information Technology in Biomedicine, 16(3): 454-462.

Zhang, S., McClean, S.I., Scotney, B.W., Hong, X., Nugent, C.D., & Mulvenna, M.D. (2010). An Intervention Mechanism for Assistive Living in Smart Homes. Journal of Ambient Intelligence and Smart Environments (Smart Home Thematic Issue), Volume 2,  Issue 3, pp. 233-252.

Munoz-Gama, J., Martin, N., Fernandez-Llatas, C., Johnson, O.A., Sepúlveda, M., Helm, E., Galvez-Yanjari, V., Rojas, E., Martinez-Millana, A., Aloini, D. and Amantea, I.A., 2022. Process mining for healthcare: Characteristics and challenges. Journal of Biomedical Informatics, 127, p.103994.

L Yang, S McClean, M Donnelly, K Burke, K Khan (2022).  A multi-components approach to monitoring process structure and customer behaviour concept drift. Expert Systems with Applications.

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 Sally McClean

Other supervisors

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