A generative AI model helps athletes avoid injuries and recover faster
By merging biomechanics and generative AI, UC San Diego’s BIGE model has the potential to transform exercise science and injury prevention for all athletes.
Researchers at the University of California, San Diego, have developed a generative AI model designed to prevent sports injuries and assist rehabilitation.
The system, named BIGE (Biomechanics-informed GenAI for Exercise Science), integrates data on human motion with biomechanical constraints such as muscle force limits to create realistic training guidance.
BIGE can generate video demonstrations of optimal movements that athletes can imitate to enhance performance or avoid injury. It can also produce adaptive motions suited for athletes recovering from injuries, offering a personalised approach to rehabilitation.
The model merges generative AI with accurate modelling, overcoming limitations of previous systems that produced anatomically unrealistic results or required heavy computational resources.
To train BIGE, researchers used motion-capture data of athletes performing squats, converting them into 3D skeletal models with precise force calculations. The project’s next phase will expand to other types of movements and individualised training models.
Beyond sports, researchers suggest the tool could predict fall risks among the elderly. Professor Andrew McCulloch described the technology as ‘the future of exercise science’, while co-author Professor Rose Yu said its methods could be widely applied across healthcare and fitness.
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