Biomechanical digital twin and genetic characterisation in rehabilitation

A biomechanics laboratory digital twin combining inertial sensors, artificial intelligence and cloud computing for personalised rehabilitation and performance optimisation in sports medicine.

The project integrates machine learning algorithms for the predictive modelling of injury risk — muscular, ligamentous and metabolic fatigue — by identifying the weight of the key features for exercise prescription.

It combines continuous biomechanical analysis with polygenic risk profiles and epigenetic markers to individualise recovery and prevention, both in high-performance athletes and in clinical patients. It cross-references personalised health, data science and Clinical processes and governed AI.

It is being developed in collaboration with a group specialising in sports biomechanics and with the innovation area of an elite sports club.