Activity recognition using digital frame streams for monitoring rehab period

Eligibility: UK/International (including EU) graduates with the required entry requirements

Funding details: Bursary plus tuition fees (UK/International - including EU at international rates from September 2021)

Duration: Full-Time – between three and three and a half years fixed term

Application deadline: 2nd June 2021

Interview date: Will be confirmed to shortlisted candidates

Start date: September 2021

For further details contact: Dr Sara Sharifzadeh

Cotutelle PhD Programme

This studentship is part of the Cotutelle PhD Programme with Deakin University (Australia). Find out more about our global Cotutelle opportunities.

Find out more

Introduction

This fully-funded PhD project is part of the Cotutelle arrangement between Coventry University, UK and Deakin University, Melbourne, Australia.

The successful applicant will spend the first year at Coventry University, the following year at Deakin University, and the final 1.5 years at Coventry University.

Project details

According to the World Health Organization (WHO), 15% of the world's population, experience disability of whom 2-4% experience significant difficulties in functioning.

Rehabilitation treatment is a necessary health intervention for disabled patients. However, according to WHO, there are only less than ten skilled rehabilitation practitioners per million population. Due to the lack of rehabilitation therapists, patients need to perform designated rehabilitation exercises at home. In addition, rehabilitation for older people in terms of mobility and self-care without the assistance of another person is important for their health and wellbeing.

The aim is to adopt AI techniques, to investigate how smart automatic rehabilitation monitoring algorithms can be formed using video and sequential frame data for disabled patients and aged people. The project has three objectives:

  1. To develop activity recognition models for monitoring the patient's rehabilitation exercise, using video/image analysis techniques.
  2. To evaluate the patient's daily movements e.g. athletic performance and quantify the evaluation results, giving feedback scores and corrective or warning guidance for progression in rehab exercise.
  3. To investigate on developing non-intrusive activity monitoring algorithms during rehabilitation using low-resolution digital sensors e.g. Infra-Red.

Funding

This is a fully-funded studentship, including:

  • Full tuition fees (for up to 4 years)
  • A stipend for up to 3.5 years subject to satisfactory progress
  • A one-time economy return airfare to host institution
  • Conference allowances

Benefits

The Research Centre for Data Science (CDS) aims to develop cutting edge pure research in artificial intelligence, data science and future computing, linking fundamental science to real-world applications.

Data science deals with the analysis and exploitation of large amounts of data (Big Data) drawing together disciplines as diverse as Computer Science, Artificial Intelligence, Statistics and Mathematics. The centre is organised according to three key themes:

  • Machine Learning for Big Data
  • Wireless Sensors and Internet of Things
  • Computational and Statistical Modelling
  • Advanced Computing

This project is addressing an important problem in assistive, automatic rehabilitation monitoring in healthcare domain. The results of this project allows further understanding about developing smart models for monitoring rehabilitation exercises and activities. The results of this project has great impact and helps the stakeholders such as medical organizations, care homes and the housing companies to utilise the project findings such as system design and AI algorithms.

On the other hand, employing such AI techniques, strengthens the candidate skills and support the candidate for future career.

In addition, the successful candidate will receive comprehensive research training including technical, personal and professional skills.

All researchers at Coventry University (from PhD to Professor) are part of the Doctoral College and Centre for Research Capability and Development, which provides support with high-quality training and career development activities.

Entry requirements

Applicants must meet the admission and scholarship criteria for both Coventry University and Deakin University for entry to the cotutelle programme.

This includes:

  • Applicants should have graduated within the top 15% of their undergraduate cohort. This might include a high 2:1 in a relevant discipline/subject area with a minimum 70% mark (80% for Australian graduates) in the project element or equivalent with a minimum 70% overall module average (80% for Australian graduates).
  • A Masters degree in a relevant subject area, with overall mark at minimum Merit level. In addition, the mark for the Masters dissertation (or equivalent) must be a minimum of 80%. Please note that where a candidate has 70-79% and can provide evidence of research experience to meet equivalency to the minimum first-class honours equivalent (80%+) additional evidence can be submitted and may include independently peer-reviewed publications, research-related awards or prizes and/or professional reports.
  • Language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component).
  • The potential to engage in innovative research and to complete the PhD within a prescribed period of study.

For an overview of each University’s entry requirements please visit:

Please note that it is essential to be able to physically locate to both Coventry University (England) and Deakin University (Australia).

Additional requirements:

  • This PhD is suitable for candidates with background in data analysis, computer science and relevant discipline.
  • A good knowledge of linear algebra, machine learning and AI concepts, linear/non-linear systems, and willingness to quickly learn further in this area.
  • Digital sensors signal and data acquisition is an advantage but is not mandatory.
  • Good programming skills in MATLAB/Python.
  • Good writing and interpersonal/communication skills.

How to apply

To find out more about the project, please contact Dr Sara Sharifzadeh.

All applications require full supporting documentation, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project.

Please note that applications must be made to both universities.

What is the application process?

Applications are submitted to both institutions. Applicants must ensure they meet eligibility requirements. Selection involves academic staff from both institutions. Shortlisted applicants will be interviewed by a panel including academic staff from each institution. Applicants will need to submit copies of certificates to both institutions in line with their respective requirements.

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