This project aims to enhance the quality of life of patients with amputees, spinal cord injury, stroke and brain injury by developing advanced control strategies for artificial limbs. This project focuses on multimodal control of prostheses using a different range of noninvasive physiological signals including EEG, EMG and accelometer. The multimodal control will be adapted based on the abilities and needs of patients. Moreover, automatic intelligent algorithms will be developed in to make the control intuitive, natural and adaptive. Such that the model can learn new movements/skills over time.
In stroke and spinal cord injury patients, in addition to artificial limb, Functional Electrical Stimulation (FES) can be potentially used to restore impaired limb motor functions. FES is a technique which stimulates intact peripheral nerves to generate muscle contractions. This is a collaborative project between department of automatic control and systems engineering and medical colleagues from Royal Hallamshire hospital and Northern General hospitals as well as colleagues in other UK universities.
The prospective student will gain experience across different disciplines. The project involves designing and conducting experimental research as well as data analysis and algorithm development.
This is a self-funded research project.
Students with either an undergraduate honours degree (2:1 / 1st) or MSc (Merit or Distinction) in engineering, mathematics, neuroscience, computer science or subjects where signal processing may be applied are encouraged to apply. If you are interested in this project, and are unsure about whether you have the right background, please get in touch. The project can be adapted based on the student’s interest and experiences.
Applicants can apply for a Scholarship from the University of Sheffield but should note that competition for these Scholarships is highly competitive: View Website
Full details of how to apply can be found at the following link: https://www.sheffield.ac.uk/acse/research-degrees/applyphd
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