Remote Gait Evaluation for People with Parkinson’s Disease

Description

This project will create an innovative method to acquire quantitative data on Parkinson’s Disease motor symptoms. The method will enable regular and remote patient monitoring, to support current standard clinical practice.

Parkinson’s Disease affects one in 500 people and can progressively affect biomechanics. Symptoms, including tremor, rigidity, bradykinesia, gait and balance impairment may typically be treated pharmacologically and with Deep Brain Stimulation. It is crucial that clinicians regularly monitor the progression of the disease to select the most appropriate treatment, especially regarding Deep Brain Stimulation. Indeed, this intervention requires detailed monitoring and assessment of the symptoms, particularly gait and balance, before making a decision regarding Deep Brain Stimulation eligibility. Subsequently, clinicians need to monitor the treatment efficacy during the disease course. This is typically done face-to-face using qualitative clinical scales such as the Unified Parkinson’s Disease Rating Scale. There is a need for remote collection of quantitative data to monitor and manage patients, e.g., to optimise treatment. This will be useful in any situation where face-to-face consultation is too difficult (e.g., during a pandemic) or too costly.

Objectives:

1.      To develop a simple, quick and clinically relevant motor task that patients can do at home. This will be based on clinical scales, literature, clinical expertise, and a PPI group.

2.      To collect data during this task using a phone app.

3.      To use statistical methods and Machine Learning to extract clinically useful information from the recordings (initiation, freezing, bradykinesia, tremor etc.).

4.      To validate the outcomes against gait-lab techniques and clinical assessment in a lab setting.

5.      To assess the clinical use of the developed system.

6.      The final step is the implementation of the algorithm into a bespoke app.

Three types of data will be collected synchronously: (1) inertial sensor data using a smartphone, (2) gait lab data and (3) clinical assessment. The challenge is information extraction for which we will use Machine Learning. Gait lab motion capture will allow validation. The project will yield data on tremor, gait and balance to inform clinicians. Finally, the app-based data will be used alongside current practice in a cohort of patients with Deep Brain surgery to test clinical applicability.

Training will be provided throughout the study in several ways. Project-specific hands-on training will be provided by the supervisory team and colleagues as needed and following regular Development Needs Analysis. This will include lab inductions, health and safety training, seminars, outreach opportunities and journal clubs. The student will be engaging intensively with the Doctoral Training Network which provides additional training. As a member of the Liverpool Doctoral College, a wide range of additional training resources will be available. The student will have regular (at least monthly) formal meetings with the supervisory team and yearly meetings with two assigned Academic Advisors.

The University is fully committed to promoting equality and diversity in all activities. In recruitment we emphasize the supportive nature of the working environment and the flexible family support that the University provides. The Institute holds a silver Athena SWAN award in recognition of on-going commitment to ensuring that the Athena SWAN principles are embedded in its activities and strategic initiatives.

We are looking for a self-motivated candidate with a 2.1 or 1st class degree in biomedical engineering, or with equivalent relevant expertise. The candidate should have analysis and programming skills, ideally in Machine Learning, and be keen to learn the biomechanical and clinical skills needed for the study.

A stipend, bench fees and full tuition fees at the UK domestic rate will be provided by the project. Due to a recent change in UKRI policy, this is now available for Home, EU or international students to apply. However, please be aware there is a limit on the number of international students we can appoint to these studentships per year.

Enquiries to: Dr Kristiaan D’Août, 

To apply:  please send your CV and a covering letter to  please put Technologies for Healthy Ageing in the subject line

Expected interviews in April 2021

Availability

Open to students worldwide

Funding information

Funded studentship

This studentship is funded by the EPSRC DTP scheme and is offered for 3.5years in total. It provides full tuition fees and a stipend of approx. £15,609 tax free per year for living costs. The stipend costs quoted are for students starting from 1st October 2021 and will rise slightly each year with inflation.
The funding for this studentship also comes with a budget for research and training expenses of £1000 per year, and for those that are eligible, a disabled students allowance to cover the costs of any additional support that is required.

Supervisors

References

Lakany, H., Extracting a diagnostic gait signature. Pattern Recognition, 2008. 41(5): p. 1627-1637.
Macerollo, A., et al. Subthalamic nucleus deep brain stimulation for Parkinson's disease: current trends and future directions. Expert Rev Med Devices. 2020 Oct;17(10):1063-1074.;
Macerollo et al. Dopaminergic treatment modulates sensory attenuation at the onset of the movement in Parkinson's disease: A test of a new framework for bradykinesia. Mov Disord. 2016 Jan;31(1):143-6.