Wearables and AI for Detection of Parkinsons Disease

Summary

​Whilst we know we cannot treat Parkinsons Disease (PD) we are also aware that we can delay the effects of the disease with proper care and treatment. Therefore early detection of symptoms for PD is very important.   This project proposes data analytics, AI and deep learning based multimodal Parkinson’s detection using sensor data from a range of technologies.

​This PhD project objective is to develop a deep learning based early detection of PD that will support clinicians.  This interdisciplinary project will create an innovative system for the early detection of PD using gait analysis. This project offers a unique opportunity to work alongside and under the supervision of experts in deep learning, gait analysis, and clinicians.

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
  • A comprehensive and articulate personal statement

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 £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

Jarchi, D., Pope, J., Lee, T.K., Tamjidi, L., Mirzaei, A. and Sanei, S., (2018). A review on accelerometry-based gait analysis and emerging clinical applications. IEEE reviews in biomedical engineering, 11, pp.177-194.

Guo, Y., Yang, J., Liu, Y., Chen, X., & Yang, G. Z. (2022). Detection and assessment of Parkinson’s disease based on gait analysis: A survey. Frontiers in Aging Neuroscience, 837.

Yogarajah, P., Chaurasia, P., Condell, J., & Prasad, G. (2015). Enhancing gait based person identification using joint sparsity model and l1-norm minimization. Information Sciences, 308, 3-22.

Jun, K., Lee, S., Lee, D.W. and Kim, M.S., (2021). Deep learning-based multimodal abnormal gait classification using a 3D skeleton and plantar foot pressure. IEEE Access, 9, pp.161576-161589.

Matsushita, Y., Tran, D. T., Yamazoe, H., Lee, J. H. (2021). Recent use of deep learning techniques in clinical applications based on gait: a survey. Journal of Computational Design and Engineering, 8(6), 1499-1532.

The Doctoral College at Ulster University

Key dates

Submission deadline
Monday 27 February 2023
04:00PM

Interview Date
18 April 2023

Preferred student start date
18 September 2023

Applying

Apply Online  

Contact supervisor

Professor Joan Condell

Other supervisors

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