Biodynamic Analysis of Alpine Skiing with a Skier–Ski–Snow Interaction Model

Purpose: – This study aimed to develop a musculoskeletal–environment interaction model to reconstruct the dynamic–interaction process in skiing. Methods: – This study established a skier–ski–snow interaction model that integrated a 3D full-body musculoskeletal model, a flexible ski model, a ski boot model, a ski–snow contact model, and an air resistance model. An experimental method was developed to collect kinematic and kinetic data using inertial measurement units, GPS, and plantar pressure measurement insoles, which were cost-effective and capable of capturing motion in large-scale field conditions. The ski–snow interaction parameters were optimized for dynamic alignment with snow conditions and individual turning techniques. Forward–inverse dynamics simulation was performed using only the skier’s body segment kinematics as the model input, leaving the pelvis’s translational degrees of freedom relative to a fixed reference frame unconstrained. The model’s effectiveness was verified by comparing the simulated results with experimental GPS and insole force data. A forward–muscular inverse–skeletal framework was used to estimate muscle activations. Results: – The agreement between simulated ski–snow contact forces and measured insole forces showed a correlation coefficient of 0.94, with a mean error of −0.022 ± 0.186 N per body weight (mean ± SD), and the error between the predicted motion trajectory and the GPS data was 0.02 ± 0.07 m. Kinematic and kinetic parameters extracted from skiers of different skill levels enabled quantitative evaluation of skiing performance. Conclusions: – The skier–ski–snow interaction model, combined with the ski–snow interaction parameter optimization, enabled the characterization of skiing characteristics across varied snow conditions and different turning techniques (such as carving and skidding). Our research advanced the understanding of alpine skiing dynamics by enabling the identification of skill-dependent kinetic patterns, thereby providing insights to enhance performance.