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Smart Hallway - The use of AI in patient monitoring

  • Full or part time
    Prof H Liu
  • Application Deadline
    Sunday, February 23, 2020
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

Applications are invited for a fully funded three-year PhD to commence in October 2020.

This PhD studentship is 1 of 6 PhD studentships funded by The University of Portsmouth in the area of biomaterials and bioengineering.

The PhD will be based in the Faculty of Technology, and will be supervised by Professor Liu, and Professor Kyberd.

You will use image analysis coupled with AI to automatically identify a person’s limbs as they walk along an institutional hallway, calculating movement-related measures, and describing a person’s mobility level. This information could be used to determine fall risk, identify changes in dementia level, or help determine if a person is ready for discharge from the hospital after surgery.

The work on this project could involve:
-Setting up a Smart Hallway
-Taking data from unimpaired persons
-Analyzing the video data
-Producing reports based on the findings

Project description
This project will use AI tools such as deep learning to extract determinants of the walking cycle from video data and then use deep learning techniques to work out how a person is walking. The longer-term aim is to use data obtained from our partners in the UK and Canada to look at persons with impairments and then detect differences between them and the unimpaired population. The nature of the work is to gather substantial amounts of data and then analyse it, initially manually dividing the data into blocks (segmenting) based on context, so that the computer can be trained on it. Additionally, the software to perform the analysis needs to be written and tested.

They will need to be able to determine the best set up for the cameras to be able to take useable data. Once they have established this through taking data on unimpaired subjects and performing simple analysis on the data. They will then have to take larger datasets in order to process the data and see which of the important variables of the walking cycle they can detect reliably. The movement outcomes can be used to generate new AI models that better link the person’s quality of movement to disease and mental health progression, effects of medication changes, or recovery after a healthcare interventions.

The student jointly be in Computing and Electronics, but will work with others in disciplines as diverse as Sport and Exercise Science and Biology to understand the problems and facilitate solutions.

General admissions criteria
You will need a good first degree from an internationally recognised university (minimum upper second class or equivalent, depending on your chosen course) or a Master’s degree in an appropriate
subject. In exceptional cases, we may consider equivalent professional experience and/or
Qualifications. English language proficiency at a minimum of IELTS band 6.5 with no component score below 6.0.

Specific candidate requirements
Mechanical engineering, Biomedical engineering. Data Analysis
Biomedical Engineering, Mechanical Engineering

How to Apply
We’d encourage you to contact Professor Peter Kyberd () to discuss your interest before you apply, quoting the project code.

When you are ready to apply, you can use our online application form. Make sure you submit a personal statement, proof of your degrees and grades, details of two referees, proof of your English language proficiency and an up-to-date CV. Our ‘How to Apply’ page offers further guidance on the PhD application process.

If you want to be considered for this funded PhD opportunity you must quote project code COMP5040120 when applying.

Funding Notes

Candidates applying for this project may be eligible to compete for one of a small number of bursaries available. Successful applicants will receive a bursary to cover tuition fees at the UK/EU rate for three years and a stipend in line with the UKRI rate (£15,009 for 2019/2020).Bursary recipients will also receive a £1,500 p.a. for project costs/consumables.

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