Postgraduate research project

Biomedical control systems

Funding
Competition funded View fees and funding
Type of degree
Doctor of Philosophy
Entry requirements
2:1 honours degree View full entry requirements
Faculty graduate school
Faculty of Engineering and Physical Sciences
Closing date

About the project

Developing effective, inexpensive systems for stroke rehabilitation is an urgent, worldwide problem.  This project will develop rehabilitation systems that use functional electrical stimulation (FES) to artificially activate muscles via surface electrodes placed on the skin. You will design the controllers and  test them with patients in collaboration with physiotherapists and clinicians.

Every year 12.2 million people suffer from their first stroke. Approximately 70% of survivors report impaired upper-limb function, and 40% are left with a permanent arm disability. Functional electrical stimulation (FES) offers a solution to regaining lost arm function, and comprises a sequence of electrical pulses that are applied using electrodes to artificially activate muscles. Unfortunately most commercial systems use open loop or triggered controllers, meaning they don’t support accurate arm movements. 

Your will develop the next generation of controllers that combine learning, adaption and intelligence to enable people to re-gain their lost hand and wrist function. You will have access to our new Control Lab facilities for testing, as well as our Printed Electronics and Materials Lab for manufacturing new wearable electrode designs. You will work with physiotherapists and clinicians in the School of Health Sciences to evaluate your complete system with stroke participants. 

Possible PhD projects in this area include: 

  • control of FES arrays for hand and wrist motion
  • integration of electrical stimulation and soft robotics for rehabilitation
  • soft robotic wearable systems for motion assistance
  • modelling of human motor control and learning
  • development of a wireless sleeve to suppress pathological tremor
  • robotics and control for wearable FES-based stroke rehabilitation
  • wearable electrical stimulation clothing for stroke rehabilitation
  • sensing and classification of human movement using electromyographic electrode arrays and artificial intelligence