Biomechanics-driven deep reinforcement learning improves Wushu skill acquisition in physical-education students over an eight-week controlled intervention

Wushu instruction still rests on the coach’s eye and on repeated demonstration, an approach that resists quantification, delivers feedback only after a routine ends, and cannot retune difficulty for each learner. We therefore asked a deliberately narrow question: does a training paradigm in which biomechanically grounded features drive a reinforcement learning policy improve skill acquisition more than conventional coaching? Four directional hypotheses, stated before analysis, framed the test.

Open-source inertial sensing was fused with markerless pose estimation to recover joint kinematics and inverse-dynamic torques, and a joint-angle-torque coupling scheme encoded those descriptors as a structured state tensor. A proximal-policy-optimization agent, driven by a composite reward and a dynamic difficulty-adjusting function, selected the training difficulty and the feedback shown to each learner. Sixty physical-education students completed an eight-week controlled intervention that compared the adaptive system with conventional coaching, with assessments at weeks 0, 4 and 8.

Across sessions the instrumented measures proved reliable, with intraclass correlation coefficients from 0.82 to 0.91, and expert scoring showed good-to-excellent agreement both between raters, 0.91, and within raters, 0.93. Linear mixed-effects models with a random intercept for participant returned significant group-by-time interactions on all four skill dimensions: movement accuracy, \(F\)(2, 116) = 18.42, \(p\)