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PhD in Biomedical Engineering

University of Salford - University of Salford and Shandong BetR Medical Technology Co. Ltd.

Qualification Type: PhD
Location: Salford
Funding for: UK Students
Funding amount: £15,824
Hours: Full Time
Placed On: 22nd May 2019
Closes: 30th June 2019

Development of a neuro-prosthesis for hand-arm rehabilitation after stroke 

University of Salford and Shandong BetR Medical Technology Co. Ltd.

The studentship is fully funded and includes:

  • A fee waiver
  • A stipend of  £15,824 p.a. for three and a half years
  • All bench fees and consumable costs
  • Funds specifically allocated for conference travel

Final date for applications: 30th June 2019

Interviews in July 2019

Description:

After a stroke many people cannot use their affected arm. Intensive physiotherapy can help, but the limited availability of physiotherapists means that home-based rehabilitation systems are needed. Functional Electrical Stimulation (FES) of muscles is a high-tech but potentially low-cost solution, which directly activates paralysed muscles through electrical stimulation via skin-surface electrodes. Adjusting the stimulation applied is difficult because the patient will have some ability to voluntarily contract their muscles and the FES must provide enough support but not too much, so that the patient is continually challenged whilst achieving the training exercises.

The system we envisage would sense arm movement but, rather than using traditional trajectory following feedback control, it will estimate the level of voluntary effort achieved by the patient and adapt muscle stimulation to compensate for their improving performance or fatigue. At present FES control parameters are adjusted manually, by trial and error, and it is unclear how this can be formalised so that it can be automated. To solve this problem you will:

  1. Use a heuristic rule-based system informed by knowledge elicitation from a group of FES specialists.
  2. Use machine-learning methods that can automatically learn from FES specialists.
  3. Apply the algorithms developed in i) and ii) in an iterative learning manner to track the patient's changing voluntary effort.

Working closely with our industrial partner, Shandong BetR Medical, and also our team of research physiotherapists, you will produce and test a prototype neuro-prosthesis based on your new learning algorithms. Shandong BetR Medical is a small spin out company led by Dr Mingxu Sun who was a researcher in our team at Salford up until 2018.     

Your PhD will build upon our previous work on FES, for example follow these links: https://www.frontiersin.org/articles/10.3389/fneur.2019.00227/full https://www.frontiersin.org/articles/10.3389/fnins.2018.00449/full        

You will join a vibrant research group, which currently holds around £8 million in research funding and hosts the UK’s Centre for Doctoral Training in Prosthetics and Orthotics. For more information, see https://www.salford.ac.uk/research/health-sciences/research-groups/human-movement-technologies

For the latest news on our group, see https://twitter.com/UoSRehabTech

Candidates:

You should have a strong interest in Biomedical Engineering, including prototyping and testing of solutions that include sensing, stimulation hardware, and embedded software. Candidates should have a first or upper second-class honours degree in engineering, physics, mathematics or computer science. Candidates with other closely related first degrees should contact Prof Howard to discuss their eligibility.

For full details of student requirements and specification please visit: http://www.salford.ac.uk/ktp/industrial-case-studentships/vacancies 

Informal enquiries may be made to Prof David Howard by email: d.howard@salford.ac.uk

A curriculum vitae and supporting statement, explaining your motivation and interests, should be sent to both V.L.Beckett@salford.ac.uk and d.howard@salford.ac.uk.

   
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