Prevention
Prevention in sports research builds on our expertise in biomechanics and machine-learning-based analyses to identify early warning signs of potential injuries, enhance adaptive capabilities, and optimize motor skills and coordination.
Research focus
Prevention in sports research moves from our expertise in biomechanics, and leverages advanced machine-learning-based approaches to safeguarding athletes using cutting-edge technology, such as motion sensors, for injury prevention strategies. Machine learning contributes by analysing data on sensory, motor and cognitive performance, enabling the identification of subtle patterns as early warning signs of potential injuries. We enhance the athletes’ adaptive capabilities, ultimately optimising motor skills and coordination to reduce the risk of injuries.
Injury prevention across the lifespan research contributes significantly to developing comprehensive and adaptable recovery and rehabilitation protocols. Our expertise in machine learning’s processing of large datasets allows the determination of age-specific risk factors and injury patterns that inform the creation of tailored preventive measures.
The societal impact is observed in promoting sustained physical activity across diverse age groups, reducing the long-term consequences of injuries and fostering a lifelong commitment to healthy participation in sports and physical activities.
The PODiaCar Project
PODiaCar brings together healthcare and academic partners across Europe to address childhood obesity and its long-term health consequences. The project develops innovative tools and educational initiatives to support prevention, early detection and healthier lifestyles.
25,000+ stakeholders engaged
European research consortium
1 digital twin ecosystem for personalised prevention
2 major risks targeted: cardiovascular disease and type 2 diabete
The researcher behind PODiaCar
Have any question about the research project? Our project contact will be happy to provide more information, discuss collaboration opportunities or answer your enquiries.
Dr. Camilo Corbellini
ccorbellini@lunex.lu
PRYSMA Project
PRYSMA is an innovative research project that uses biomechanics, artificial intelligence and advanced motion analysis to better understand and prevent overuse injuries in football. By transforming complex performance data into practical injury risk indicators, the project aims to support safer training, improved performance and long-term athlete health.
1 in 3 injuries are overuse injuries
Multiple data sources integrated into one platform
Football-focused prevention research
Data-driven decision support for coaches
The researcher behind PRYSMA
Have any question about the research project? Our project contact will be happy to provide more information, discuss collaboration opportunities or answer your enquiries.
Serena Pizzocaro
spizzocaro@lunex.lu