Summary

It is widely accepted that systemic reform of healthcare across the UK and Ireland is needed. Recent research has put particular emphasis on the need for improved preventative and community care, particularly post-COVID.  The combination of Artificial Intelligence and Wearable Sensors presents a potential solution for improved preventative community care. These combined technologies could enable more cost effective, remote and personalized health care leading to improved health outcomes.  The ability to self-monitor behaviour and health using wearable sensors has also created new opportunities for people to actively participate in their healthcare in non-clinical settings. As a case study we will consider community and remote care of diabetics.

Diabetes is one of the biggest challenges facing health systems globally. Worldwide, the number of people affected by diabetes is rapidly increasing due to aging populations and sedentary lifestyles, with 500 million cases predicted in 2030 [1]. Recent advancements in wearable technology have revolutionised the management of diabetes. Frequent measurement of glucose concentration (GC) is necessary to monitor and prevent diabetes-related complications [2].

The development of Continuous Glucose Monitoring (CGM) sensors now allow for real time measurement of GC. Regular GC measures empower the ability of making decisions concerning therapeutic actions and overall have been shown to improve quality of life. Accurate predictions of the future GC trajectory offer important information for making meal, activity and insulin dosing decisions. Research has also shown that physical activity and physiological measurements can improve the accuracy of predicting GC. In addition to sensors measuring GC, recent research has shown that measurements such as physical activity and improved mental health can contribute to a better understanding GC and improved therapeutic actions [3, 4].

The aim of this project will be to investigate multiple sensor modalities and their potential to improve Diabetes management. Modern wrist worn sensors include sensors to measure physical activity, oxygen saturation (SpO2), ECG and Heart Rate. Combined with CGM sensors, there is potential opportunity to improve the management of diabetes with more accurate measurement of health and GC. The project will build on state-of-the-art research by the supervisors in areas of AI, digital health and wearable sensors [5].

The core aim of the project will be to develop Artificial Intelligence techniques that can extract meaningful information from wearables data, related to Diabetes health - from a range of sensor modalities. The technology will be utilized in unsupervised free-living conditions. Thus, a fundamental AI problem will need to be solved in relation to how the large amounts of data collected can be leveraged to train AI models using the weak and noisy ground truth data collected in real world conditions.


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.

  • Experience using research methods or other approaches relevant to the subject domain
  • Research proposal of 1500 words detailing aims, objectives, milestones and methodology of the project
  • A demonstrable interest in the research area associated with the studentship

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

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 £15,840 (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.

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 £15,840 (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

  1. Cappon, G., Acciaroli, G., Vettoretti, M., Facchinetti, A. and Sparacino, G., 2017. Wearable continuous glucose monitoring sensors: a revolution in diabetes treatment. Electronics, 6(3), p.65.
  2. G. Sparacino et al., "Glucose concentration can be predicted ahead in time from continuous glucose monitoring sensor time-series", IEEE Trans. Biomed. Eng., vol. 54, no. 5, pp. 931-937, May 2007.
  3. Whelan, M.E., Orme, M.W., Kingsnorth, A.P., Sherar, L.B., Denton, F.L. and Esliger, D.W., 2019. Examining the use of glucose and physical activity self-monitoring technologies in individuals at moderate to high risk of developing type 2 diabetes: randomized trial. JMIR mHealth and uHealth, 7(10), p.e14195.
  4. Sevil, M., Rashid, M., Hajizadeh, I., Park, M., Quinn, L. and Cinar, A., 2021. Physical Activity and Psychological Stress Detection and Assessment of Their Effects on Glucose Concentration Predictions in Diabetes Management. IEEE Transactions on Biomedical Engineering.
  5. Kelly, D., Condell, J., Curran, K. and Caulfield, B., 2020. A multimodal smartphone sensor system for behaviour measurement and health status inference. Information Fusion, 53, pp.43-54.

The Doctoral College at Ulster University