Clinical movement analysis provides quantitative information on patients with movement disorders in order to aid clinical decision making when managing their conditions. Current procedures require extensive laboratory facilities to undertake such analyses, including usually the use of 3D optical movement analysis cameras, muscle activity measurement and force transducers for the measurement of interaction forces between people and the environment. The data recorded in such sessions are often used as the inputs to musculoskeletal models, allowing internal variables that cannot be measured to be estimated, for example joint contact forces, or forces in muscles and ligaments (e.g. Bolsterlee et al. 2013, Ameln et al. 2019).
Recently, other methods for solving models of human movement have been described, allowing less complete datasets or data from other sensors to be used to analyse movement, and faster prediction of unmeasured movements that could be used to aid clinical planning (van den Bogert et al. 2011). The majority of work in this area has focussed on the lower limb (e.g. Dorschky et al. 2019), and indeed biomechanical computer models of the lower limb are often used to inform surgical treatment options in children with cerebral palsy. For the upper limb, such models have not reached similar widespread use, but have been shown to help in the understanding of fundamental biomechanical principles.
As a team, we have many years of experience in biomechanics and computer modelling of the upper limb, with more than 40 peer-reviewed publications in related areas (https://scholar.google.com/citations?user=Gf4QzU4AAAAJ&hl=en). Our models have been applied to clinically important problems such as the restoration of arm function in spinal cord injury (Chadwick et al., 2011), estimation of internal loading for prosthesis design, and analysis of upper limb function in manual wheelchair users.
The aim of this project will be to improve biomechanical models of the upper limb, implementing new solution methods involving optimal control, to allow clinically useful data on upper limb movement to be generated from wearable sensors such as inertial measurement units. High quality movement data from wearable sensors could increase the availability of clinical movement analysis to clinics at reduced cost and with reduced initial outlay.
Selection will be made on the basis of academic merit. The successful candidate should have, or expect to obtain, a UK Honours degree at 2.1 or above (or equivalent) in Mechanical or Biomedical Engineering, Human Movement Science or related area. A relevant MSc degree (passed with Merit) will be an advantage.
Formal applications can be completed online: https://www.abdn.ac.uk/pgap/login.php
• Apply for Degree of Doctor of Philosophy in Engineering
• State name of the lead supervisor as the Name of Proposed Supervisor
• State ‘Self-funded’ as Intended Source of Funding
• State the exact project title on the application form
When applying please ensure all required documents are attached:
• All degree certificates and transcripts (Undergraduate AND Postgraduate MSc-officially translated into English where necessary)
• Detailed CV, Personal Statement/Motivation Letter and Intended source of funding
It is possible to undertake this programme by distance learning. Access to a good quality computer suitable for running Matlab will be required. For guidelines on system requirements, see https://uk.mathworks.com/support/requirements/matlab-system-requirements.html.
Informal inquiries can be made to Dr E Chadwick ([Email Address Removed]) with a copy of your curriculum vitae and cover letter. All general enquiries should be directed to the Postgraduate Research School ([Email Address Removed])