Parkinson’s disease is associated with difficulty and slowness in turning which may predict falls in this population. In addition to the debilitation caused by falling, the chronic effects are often associated with immense anxiety and fear and therefore represents a current challenge in healthcare. Turning is usually automatic but in Parkinson’s disease it may depend on cognitive skills and as such dual-tasking may exaggerate deficits. This project aims to explore turning in patients with Parkinson’s disease by means of motion capture, platform forces and lower limb EMG to auditory cues. When repeated, this task typically produces learning but this might also be affected by Parkinson’s disease. In a series of follow-up investigations, the successful candidate will also study other neurological conditions and monitor the progress of participants in order to find out whether any measure collected predicts future falls.
The PhD will be undertaken in the world-class facilities at Walsall Campus in the Institute of Human Sciences that comprise the latest movement analysis hardware (Qualisys and XSENS) and will be supervised by the team of Dr Tina Smith (biomechanist and movement scientist) and Dr Mitesh Patel (vestibular scientist and movement scientist). Pilot data has been collected and this PhD would be to continue this work in Parkinson’s disease and in other neurological conditions.
We welcome applications from self-funded students who are highly motivated at any time. Applicants should have a recognised BSc Honours or Masters Degree with a 2.1 or equivalent in Sports Science, Movement Science, Physiotherapy, Psychology, Medical Science, Neurophysiology or a related field. Candidates will need to be willing to work with participants and patients. Ideal candidates would be those with experience of 3D motion analysis.
Eligibility: Applicants whose entry award was not delivered in English, or is a non-native speaker on English, shall be required to demonstrate proficiency in English at least to the level of an IELTS score of 7.0 or its equivalent.