Biomechanically-constrained generative adversarial networks for motion skill simulation and error correction in virtual training systems

Conventional motor-skill training runs into three practical ceilings: expert supervision is scarce, feedback arrives late, and the fine biomechanical faults that separate proficient from elite execution often pass unnoticed. We address those ceilings with a framework that couples generative adversarial networks to explicit biomechanical constraints, so that physiological plausibility is enforced during generation rather than merely encouraged. Two

Design: choices distinguish the system. A dual-pathway discriminator judges perceptual realism and biomechanical correctness in parallel, and a hierarchical error-detection module aggregates deviations across several temporal scales before an adaptive correction stage tailors feedback to the learner’s current proficiency. Motion-capture recordings from 45 participants performing basketball free throws, bodyweight squats and vertical countermovement jumps drove training and evaluation. Measured against expert annotation, the constraint-aware model identified technique faults with 89.4% accuracy; it lowered the injury-weighted violation-severity index by 76% relative to a vanilla GAN baseline, a comparison under which the unweighted frame-wise violation rate fell from 18.7 to 2.7%; and expert raters scored the trials immediately following corrective feedback 23.7% higher on average, a within-session effect obtained without a control group. Coaches blind to model identity also rated its outputs above the baselines on biomechanical validity and instructional suitability. Embedding physics-based reasoning inside a learned generative process therefore loosens, within the three terrestrial skills examined here, the familiar trade-off between motion diversity and physiological plausibility, and points to practical directions for sports training, rehabilitation and motor-skill education.