University College London Featured PhD Programmes
University College London Featured PhD Programmes
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Birkbeck, University of London Featured PhD Programmes
University College London Featured PhD Programmes

Automatic Gait Analysis Using Artificial Intelligence based on X-ray Video Sequences


Project Description

Gait is one of the most common behaviours in humans and other animals and can be affected by disorders such as arthritis and many others. Gait analysis is widely used in human fundamental and clinical research and can be studied using motion capture. However, there is a challenge to analyse gait from a large amount of motion data in an automated manner when using markerless techniques. This project aims to develop and evaluate a new fully automatic tool for the accurate analysis of mouse gait based on high-speed X-ray video sequences by harnessing artificial intelligence techniques. The tool developed will be applied to quantify gait in a mouse model for osteo-arthritis.

The successful PhD candidate will benefit from working with a multidisciplinary team in which there exists extensive experience in the areas of computer science, image processing, biostatistics and biomechanics. The candidate will obtain key skills in image analysis and machine learning, cutting edge imaging techniques (especially biplanar X-Ray videography), applied statistical methods, biomechanics and writing conference and journal papers. He or she will work in a collaborative context and contribute to continued work in this line of research by generating data to attract further funding.

The student will receive core training from the Liverpool Doctoral College as well as the Institute of Ageing and Chronic Disease. This training spans all three years of the PGR programme, and includes Inductions (general and safety), E-learning (e.g. online AI courses), seminars (presenting as well as attending), outreach opportunities and journal clubs. The programme is flexible, and the student can tailor training to his/her needs to a large extent, including (but not limited to) opportunities detailed on the website of the Liverpool Doctoral College.

In addition to the supervisors, the student will also have two academic advisors and progress will be monitored by them during yearly meetings. The student will also have recorded meetings with the supervisory team, and will benefit from existing infrastructure to keep track of their activities. The skills the student learns will make them highly employable in the field of medical imaging, computer vision and health sciences.

The Institute of Ageing and Chronic Disease is fully committed to promoting gender equality in all activities. We offer a supportive working environment with flexible family support for all our staff and students, and applications for part-time study are encouraged. The Institute holds a silver Athena SWAN award in recognition of on-going commitment to ensuring that the Athena SWAN principles are embedded in its activities and strategic initiatives.

The successful candidate should have, or expect to have an Honours Degree at 2.1 or above (or equivalent) in Computer Science, Engineering, Physics or Mathematics. It is essential to have strong experience in signal and image analysis, machine learning computer programming (e.g., Python, C/C++ or Matlab) plus a proactive approach to their work. Candidates whose first language is not English should have an IELTS score of 6.5 or equivalent.

To apply please send your CV and a covering letter to Dr Yalin Zheng (), Dr Kris D’Aout () or Prof George Bou-Gharios () with a copy to . Expected interview date: December 2019

Key words
Artificial intelligence, computer vision, deep learning, video sequence, gait analysis

Funding Notes

This project is jointly funded by the Institute of Ageing and Chronic Disease. The studentship will include a three year stipend at a standard rate (approx. £15,009 per annum) and fees at the UK/EU rate (approx. £4,327).

Non-EU (overseas) students are encouraged to apply, but they will have to find ways to cover the difference between UK/EU student fees and foreign student fees.

References

• https://www.nature.com/articles/nature14539
• https://arxiv.org/abs/1503.04069
• https://arxiv.org/abs/1312.4659
• https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0201728
• https://www.sciencedirect.com/science/article/pii/S0168010215001637

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