Introducing Julie Emmerson
CAMERA is thrilled to be welcoming 4 new PhD researchers to our team this autumn. Julie Emmerson is joining us to work with Co-Investigator Steffi Colyer in the Department for Health.
We asked Julie to tell us a little about herself and her PhD.
“I am from Kenilworth, Warwickshire. I studied an integrated Master’s degree in Physics at Durham University, before spending two years at Lamar University in Texas on a track & field / cross country scholarship, studying a MSc in Kinesiology. I am excited that this PhD opportunity with CAMERA allows me to bring both of those areas together. In my free time, you will usually find me running and I especially love fell / mountain running. “
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 much 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. Specifically, these approaches typically estimate the centre of mass of the athlete as the centroid of their silhouette, which clearly 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, which will clearly become a much larger problem when movements across a whole training session or match (typically > 200 accelerations; 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 spontaneously 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.