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PhD Studentship - Combining embedded sensor technology and machine learning to quantify swimmers’ performance in a normal training environment

Civil, Maritime & Environmental Engineering

Location:  Boldrewood Campus
Closing Date:  Friday 01 July 2022
Reference:  1846922DA

Supervisory Team:    Dr Joseph Banks, Prof Dominic Hudson, Dr Adam Sobey, Prof Stephen Beeby

Project description

Coaching feedback systems in competitive swimming are limited by the quality of the data available, the time taken to set up equipment and post process data. This severely limits the number of training sessions that can be analysed in detail making it hard to accurately assess a swimmer’s training load and determine how their technique could be improved in the future.

This project, sponsored by British Swimming and the English Institute of Sport, will use small unobtrusive sensors embedded in swimming apparel to measure key performance metrics during normal coaching sessions. This will allow quantitative data to be obtained and processed quickly and automatically to assist coaches and biomechanists to improve athlete performance. 

Embedding the sensors into normal swimming apparel minimises the impact they have on the athlete’s technique, however current sensors of this size with adequate power supply requirements and efficient data transfer methods are not available and will require some novel approaches and development work. 

Machine learning techniques will be used to automatically classify the data into predefined activities based on the typical phases of a race and the different competition strokes. The classified datasets will allow macro-level performance indicators such as time spent in different phases and mean swimming speed per length, allowing a quantifiable measure of training load to be determined. The ultimate objective is to then develop automated performance analysis at a stroke-by-stroke level to determine key performance indicators. Initially data will be collected using club-level swimmers with currently available sensors to allow a large, unique, data set to be obtained with varying levels of athlete ability before embedding these systems into British Swimming training sessions. These datasets will allow the complex interactions between individual swimming kinematics and overall athlete performance to be analysed using AI-based techniques.

This research project is supported by British Swimming and the English Institute of Sport who have collectively supported over 15 PhD projects working with Olympic athletes within the Performance Sport Engineering Lab at the University of Southampton.   

If you wish to discuss any details of the project informally, please contact Dr Joseph Banks, Maritime Engineering Research Group, Email:, Tel: +44 (0) 2380 59 6625.

Entry Requirements

A very good undergraduate degree (at least a UK 2:1 honours degree, or its international equivalent).

Closing date: applications should be received no later than 01 July 2022 for standard admissions, but later applications may be considered depending on the funds remaining in place.

Funding: For UK students, Tuition Fees and a stipend of £16,062 tax-free per annum for up to 3.5 years. 

How To Apply

Applications should be made online. Select programme type (Research), 2022/23, Faculty of Physical Sciences and Engineering, next page select “PhD  Engineering & Environment (Full time)”. In Section 2 of the application form you should insert the name of the supervisor  Joseph Banks 

Applications should include

Research Proposal

Curriculum Vitae

Two reference letters

Degree Transcripts to date

Apply online:

For further information please contact: 


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