Biodynamic Analysis of Alpine Skiing with a Skier-Ski-Snow Interaction Model
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916478470586368 |
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| author | Gao, Nan Jin, Huitong Guo, Jianqiao Ren, Gexue Yang, Chun |
| author_facet | Gao, Nan Jin, Huitong Guo, Jianqiao Ren, Gexue Yang, Chun |
| contents | This study establishes a skier-ski-snow interaction (SSSI) model that integrates a 3D full-body musculoskeletal model, a flexible ski model, a ski-snow contact model, and an air resistance model. An experimental method is developed to collect kinematic and kinetic data using IMUs, GPS, and plantar pressure measurement insoles, which are cost-effective and capable of capturing motion in large-scale field conditions. The ski-snow interaction parameters are optimized for dynamic alignment with snow conditions and individual turning techniques. Forward-inverse dynamics simulation is performed using only the skier's posture as model input and leaving the translational degrees of freedom (DOFs) between the pelvis and the ground unconstrained. The effectiveness of our model is further verified by comparing the simulated results with the collected GPS and plantar pressure data. The correlation coefficient between the simulated ski-snow contact force and the measured plantar pressure data is 0.964, and the error between the predicted motion trajectory and GPS data is 0.7%. By extracting kinematic and kinetic parameters from skiers of different skill levels, quantitative performance analysis helps quantify ski training. The SSSI model with the parameter optimization algorithm of the ski-snow interaction allows for the description of skiing characteristics across varied snow conditions and different turning techniques, such as carving and skidding. Our research advances the understanding of alpine skiing dynamics, informing the development of training programs and facility designs to enhance athlete performance and safety. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_08056 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Biodynamic Analysis of Alpine Skiing with a Skier-Ski-Snow Interaction Model Gao, Nan Jin, Huitong Guo, Jianqiao Ren, Gexue Yang, Chun Physics and Society Emerging Technologies Computational Physics This study establishes a skier-ski-snow interaction (SSSI) model that integrates a 3D full-body musculoskeletal model, a flexible ski model, a ski-snow contact model, and an air resistance model. An experimental method is developed to collect kinematic and kinetic data using IMUs, GPS, and plantar pressure measurement insoles, which are cost-effective and capable of capturing motion in large-scale field conditions. The ski-snow interaction parameters are optimized for dynamic alignment with snow conditions and individual turning techniques. Forward-inverse dynamics simulation is performed using only the skier's posture as model input and leaving the translational degrees of freedom (DOFs) between the pelvis and the ground unconstrained. The effectiveness of our model is further verified by comparing the simulated results with the collected GPS and plantar pressure data. The correlation coefficient between the simulated ski-snow contact force and the measured plantar pressure data is 0.964, and the error between the predicted motion trajectory and GPS data is 0.7%. By extracting kinematic and kinetic parameters from skiers of different skill levels, quantitative performance analysis helps quantify ski training. The SSSI model with the parameter optimization algorithm of the ski-snow interaction allows for the description of skiing characteristics across varied snow conditions and different turning techniques, such as carving and skidding. Our research advances the understanding of alpine skiing dynamics, informing the development of training programs and facility designs to enhance athlete performance and safety. |
| title | Biodynamic Analysis of Alpine Skiing with a Skier-Ski-Snow Interaction Model |
| topic | Physics and Society Emerging Technologies Computational Physics |
| url | https://arxiv.org/abs/2411.08056 |