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Anglia Ruskin University ARU Featured PhD Programmes
Anglia Ruskin University ARU Featured PhD Programmes

On-court workload assessment of tennis players using markerless motion analysis

Department for Health

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Dr Steffi Colyer No more applications being accepted Competition Funded PhD Project (European/UK Students Only)
Bath United Kingdom Biomechanics Computer Vision Machine Learning Physiology Sport Performance

About the Project

The University of Bath is inviting applications for the following funded PhD project supervised by Dr Steffi Colyer (Department for Health), Prof Darren Cosker (Department of Computer Science), Dr Laurie Needham (Department for Health) and Dr Sean Williams (Department for Health).

Funding is available to candidates who qualify for Home fee status. Following the UK’s departure from the European Union, the rules governing fee status have changed and, therefore, candidates from the EU/EEA are advised to check their eligibility before applying. Please see the Funding Eligibility section below for more information.

The successful student will be part of the Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA) which performs world-leading multi-disciplinary research in Intelligent Visual and Interactive Technology. Funded by the EPSRC and the University of Bath, CAMERA exists to accelerate the impact of fundamental research being undertaken at the University in the Departments of Computer Science, Health and Psychology.

Overview of the Research:

The ability to accurately track on-court player movement is becoming increasingly relevant in tennis, enabling the objective quantification of player workload, which can be utilised in both performance and commercial (e.g. media statistics) applications. To date, automatic player tracking has received some attention in the literature, with several computer vision algorithms proposed (e.g. Archana & Geetha, 2015; Bloom & Bradley, 2003). However, no system has been fully validated to our knowledge and therefore there exists concerns over their accuracy for certain applications. Specifically, these approaches typically estimate the centre of mass of the athlete as the centroid of their silhouette, which is not representative of the player’s centre of mass in many poses. This will introduce a degree of error in the quantification of each movement effort, which will clearly become a much larger problem when movements across a whole training session or match (typically > 200 sprint accelerations even in youth players; Pereira et al., 2016) are of interest.

A further problem of workload monitoring systems currently adopted is the arbitrary thresholds typically used to define movement intensities (e.g. >5.5 m/s movement would be classed as sprinting for every player), which clearly do not capture the metabolic cost of an individual’s movement. To accurately track the individual workloads and make inferences about the likely fatigue an athlete is experiencing, it is paramount that such intensity domains are individualised. An automatic system capable of providing the individualised work capacity metrics would considerably improve workload monitoring of tennis players. In order to overcome the aforementioned challenges, the proposed project would validate markerless player tracking methods against gold standard marker-based motion capture and utilise these technologies alongside work capacity tests to produce an individualised workload assessment tool.

Candidate Requirements:

Applicants should hold, or expect to receive, a First Class or good Upper Second Class Honours degree (or the equivalent). A master’s level qualification would also be advantageous.

In addition, applicants should either:

1) Have a strong sports science background, with some experience in motion capture and some understanding of exercise intensity domains. Such applicants should also be willing to learn how to code and how to utilise existing computer vision algorithms.


2) Have a strong computer vision background, with some experience of motion capture. Such applicants should have a keen interest in sports science and the application of computer vision in sports performance enhancement.

Non-UK applicants must meet our English language entry requirement.

Enquiries and Applications:

Informal enquiries are welcomed and should be directed to Dr Steffi Colyer ([Email Address Removed]).

Formal applications should be made via the University of Bath’s online application form for a PhD in Health clearly stating the name of the project.

More information about applying for a PhD at Bath may be found on our website.

Funding Eligibility:

In order to be considered for the available studentship, you must qualify as a Home student. In determining Home student status, we follow the UK government’s fee regulations and guidance from the UK Council for International Student Affairs (UKCISA). Further information may also be found within the university’s fee status guidance.

EU/EEA citizens who live outside the UK are unlikely to be eligible for ‘Home’ fees and funding.

Funding Notes

A studentship includes Home tuition fees, a stipend (£15,609 per annum, 2021/22 rate) and research/training expenses (£1,000 per annum) for up to 3 years. Eligibility criteria apply – see Funding Eligibility section above.


Needham, L., Evans, M., Cosker, D. & Colyer, S. (2020). Using Computer Vision and Deep Learning Methods to Capture Skeleton Push Start Performance, Proceedings of the International Society of Biomechanics in Sports.
Evans, M., Needham, L., Colyer, S. & Cosker, D. (2020). A non-invasive vision based approach to velocity measurement of Skeleton training, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops
Colyer, S., Evans, M., Cosker, D. & Salo, A (2018). A review of the evolution of vision-based motion analysis and the integration of advanced computer vision methods towards developing a markerless system. Sports Medicine - Open. p. 1-15.
Evans, M., Colyer, S., Cosker, D. & Salo, A. (2018). Foot Contact Timings and Step Length for Sprint Training, 2018 IEEE Winter Conference on Applications of Computer Vision: WACV. p. 1652-1660.
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