Applications are invited for an exciting and innovative three-year, full time PhD studentship in association with the Badminton World Federation. This project will combine wearable technology, biomechanical signal processing, and machine learning techniques to develop and apply an open-source badminton-specific training load metric. Research equipment has been provided by both STATSports and IMeasureU to support this project.
The supervisors will be Dr Stuart McErlain-Naylor (Senior Lecturer; Sports Biomechanics) and Dr Marco Beato (Associate Professor; Strength and Conditioning). The project team will also include Mark Connor (Lecturer; Performance Analysis and Computer Science).
Practitioners in many sports use wearable technologies for training prescription and injury management. The application of back-mounted systems based on GPS or related technologies is limited in short distance indoor accelerations, sidesteps, lunges, etc.; hence limited in badminton. Applications may be aided via more applicable sport-specific training load measures based on biomechanical load-response relationships.
The initial data collection phase of this project will consist of recording controlled and random badminton-specific movements, with participants recruited from a Performance Centre partnered with the project. Activity recognition procedures will be used to identify and quantify common badminton-specific movement patterns. You will design and apply bespoke badminton-specific recognition algorithms to the processed sensor data, whilst cross-checking for accuracy using explicitly notated video footage. Following evaluation, a suitably accurate activity recognition algorithm will be used to extract a range of discrete movement patterns and further calculations will be performed to quantify the intensity, impact, and acceleration transmission experienced by players in training and competition.
This project aims to develop a simple and easily interruptible quantification of training load that is currently lacking in badminton. The main contribution of this project will be the development of a novel training load monitoring system which can be used with a wide variety of commercially available wearable technologies. This system could also be scaled to other related and important areas of badminton-specific sport science such as injury management, training prescription and performance evaluation.
Aspects of the project are open to change as the candidate moves through the PhD process, planning and conducting research. In particular, there is scope to apply the methods to a number of applied problems during the later parts of the PhD.
The successful candidate
The successful candidate will need to be passionate about the applications of biomechanics and wearable technology in sport. In addition to an understanding of biomechanics, they will have relevant programming skills and will understand the role of signal processing in the analysis of human movement. They will possess strong analytical abilities, particularly those suited to large data sets.
2.1 or higher honours degree in a relevant discipline, have good knowledge of data analytics, and have demonstrable prior skills in sports biomechanics, applied mathematics, and/or computer science. Whilst not essential, a Masters’ level degree in a related subject would be advantageous. Your prior degree may be in sports science, mathematics, physics, data analytics, computer science or engineering.
Successful candidates will be based at the University of Suffolk’s Ipswich waterfront campus, although there will be the opportunity to perform a significant proportion of work remotely if required.
Project and Application Timeline
The deadline for applications is 17:00 GMT Friday 12th November.
The project start date is January 2022.
The duration of the PhD will be for three years (full-time study).
Informal enquires about this studentship should be addressed to Dr Stuart McErlain-Naylor at [Email Address Removed]
How to apply:
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