Innovating Injury Prevention with Generative AI and Biomechanics
In a pioneering effort at UC San Diego, researchers have unveiled BIGE — Biomechanics-informed GenAI for Exercise Science — a generative AI model that promises to transform how athletes train, rehabilitate, and prevent injuries. By fusing advanced machine learning with realistic biomechanical constraints, BIGE aims to deliver motion guidance that aligns with human anatomy, potentially reducing injury risk and accelerating recovery for athletes across disciplines.
Traditional approaches to movement generation fall short when it comes to anatomical feasibility. Many AI-generated motions look convincing but violate fundamental biomechanical limits, such as joint angles, muscle forces, and torque. BIGE addresses this gap by training on real motion-capture data and grounding generated movements in credible biomechanical constraints. This combination yields videos and prompts that athletes can imitate or adapt, with a higher likelihood of transfer to real-world performance and safety.
The model was trained with extensive motion-capture footage of athletes performing squats and related exercises. Researchers translated these motions onto 3D skeletal representations and computed the forces involved, ensuring that generated movements adhere to the body’s physical constraints. The result is a set of movement patterns that are not just visually plausible but biomechanically sound.
How BIGE Works and Why It Matters
BIGE’s workflow integrates data-driven motion examples with biomechanical knowledge about muscular output, joint limits, and resistance. By incorporating these constraints, the model generates two key kinds of outputs: injury-preventive motions for training and rehabilitation-focused motions for recovery after injury. In practical terms, athletes receive video demos and technique cues that they can mirror to minimize injury risk and sustain progress during workouts, even when they are temporarily sidelined due to an injury.
“This approach is going to be the future,” said Andrew McCulloch, a distinguished professor of bioengineering at UC San Diego and a senior author of the project. The claim underscores a broader shift in sports science: leveraging generative AI to create personalized, biomechanically accurate training content at scale. BIGE is positioned as a unique solution because it merges generative capabilities with realistic human mechanics rather than relying on stylized, unconstrained motion generation.
Another advantage of BIGE is its potential efficiency. Prior methods that attempt to simulate realistic movements without AI can demand prohibitive computational resources. By focusing on movements that respect physical constraints, the model can streamline the generation process, offering athletes and coaches rapid access to tailored training plans and rehabilitation sequences.
Applications and Future Directions
The immediate applications are broad. Coaches can use BIGE to curate sport-specific warmups that minimize common injury vectors, such as knee or lower-back strain, by recommending motions that maintain joint alignment and appropriate force distribution. The model could also be used to optimize training regimens for performance gains, guiding athletes toward movements that maximize efficiency and power while preserving tissue health.
Beyond athletic performance, BIGE holds promise for vulnerable populations, including the elderly, who face higher fall risks. The researchers envision adapting the model to generate safe, protective movements that reduce the likelihood of falls and facilitate safe exercise routines for older adults.
Looking ahead, the team plans to expand BIGE to move beyond squats to a wider array of athletic motions. Personalization poses another exciting frontier: tailoring generated movements to individual athletes based on their anatomical differences, injury history, and training goals. This personalization could enable more precise rehab protocols and injury-prevention strategies, moving sports science toward truly individualized coaching at scale.
The UC San Diego team recently presented their findings at the Learning for Dynamics & Control Conference in Ann Arbor, Michigan, signaling growing interest in combining AI with biomechanics for safer, more effective exercise. As the field advances, BIGE could become a standard tool for athletes seeking optimized performance without compromising safety.
Why This Matters for Athletes, Teams, and Clinicians
For athletes, BIGE offers accessible, evidence-based insights into how to train smarter. For teams and coaches, it provides a scalable way to deliver consistent, biomechanically sound guidance across diverse athletes. For clinicians and rehabilitation specialists, the model could support safer, faster return-to-play protocols and more personalized rehab programs. In short, BIGE represents a significant step toward integrating cutting-edge AI with the everyday realities of sports medicine and performance training.
