Introducing Andrea Braschi
CAMERA are thrilled to be welcoming 4 new PhD researchers to our team this autumn. Andrea Braschi is joining us to work with Co-Investigator Dario Cazzola from the Department for Health.
We asked Andrea to tell us a little about himself and his PhD.
“My name is Andrea Braschi and I am from Rome, Italy. I started my academic career by studying Sports and Exercise Science at the University of Rome “Foro Italico” in Italy and, subsequently, moved to the UK where I earned a Master’s degree in Sport Biomechanics at Loughborough University. This project supported by CAMERA enables me to learn computationally powerful techniques of Machine Learning and implement them in the field of biomechanics. Apart from being a PhD student, I am a huge combat sport and basketball fan and practise martial arts myself.”
Spinal catastrophic injury in rugby activities (e.g. scrums and tackles) are not very common but can be very impairing (e.g. tetraplegia, or quadriplegia) and generate high societal costs. Governing bodies, such as the World of Rugby, have successfully minimised such injuries during scrummaging by changing the game rules. These changes were informed by biomechanical analysis carried out at University of Bath. Unfortunately, rugby tackling still remains a very risky part of the game and a cause-effect relationship between tackling technique and resulting injuries is still lacking. The aim of this projects is to unveil such relationship and causally link specific tackling situations to injury diagnoses. This will be achieved by using probabilistic, generative machine learning to represent the probabilistic relationship between qualitative and quantitative tackling features and injury diagnoses. This is a great opportunity to extend the state-of-the-art in machine learning whilst working directly with domain experts to provide real-world insights and benefits in the fields of bio-mechanics and sports injury prevention. Qualitative data will include description of injurious events gathered via interviews with injured players, whilst quantitative data will be retrieved from the simulation of the same injuries in a computational framework using musculoskeletal models of the spine. The simulation framework will be firstly validated against imaging data (e.g. x-rays and MRIs) of real-world spinal injuries, and then used to create a set of ‘synthetic’ data of theoretical injurious scenarios. The ‘synthetic’ datasets will include quantitative information of vertebrae motion, and forces applied to the spine during injurious events, which is key to understand the injury mechanisms and compare the simulated injuries with real injury diagnoses and medical images. The final step will consist in the creation of the probabilistic model that will use as input the qualitative descriptors of tackling scenarios and quantitative simulation results, and will provide the most likely injury diagnosis.