可穿戴生物力学和基于视频的轨迹分析,以提高高山滑雪的表现
Denisa-Iulia Brus1, Dorin-Ioan Cătană2
1Department of Motor Performance, Transilvania University of Brașov, 500036 Brașov, Romania.
Sensors (Basel, Switzerland)
|February 13, 2026
概括
阿尔卑斯山滑雪的表现依赖于集成的生物力学和轨迹分析. 有效的运动,而不仅仅是更短的路径,导致更快的时间,突出了结合诊断工具的需要.
科学领域:
- 运动科学 运动科学 运动科学
- 生物力学 生物力学
- 绩效分析 绩效分析
背景情况:
- 目前的高山滑雪诊断通常会单独分析轨迹几何和生物力学.
- 这种分离限制了对技术低效率和性能优化的全面理解.
- 需要综合方法来增强实时培训反.
研究的目的:
- 评估一个综合分析框架,将人工智能驱动的轨迹分析 (OptiPath) 与可穿戴惯性传感器 (XSensDOT) 结合起来.
- 通过将空间轨迹偏差与生物机械执行联系起来,以确定巨型滑雪滑雪中的技术低效率.
- 为了确定轨迹几何,生物力学变量和滑雪表现结果之间的关系.
主要方法:
- 30名年轻的高山滑雪运动员 (14-16岁) 进行了巨型斯拉洛姆赛,并控制了横向偏移.
- 用计算机视觉 (OptiPath) 分析了滑雪者的轨迹,并通过惯性测量单位 (XSensDOT) 记录了下肢/骨干动力学和加速.
- 从理想轨迹的偏差量化,并评估了生物机械执行协调,负载和加速.
主要成果:
- OptiPath准确地检测到轨迹变化,但更短的路径与更快的运行时间不一致相关.
- 优异的滑雪性能与高效的生物机械执行有关,包括协调的干部-下肢运动和受控的垂直负载.
- 较高的前进加速度和较少的横向校正是表现更好的关键指标,无论轨迹长度如何.
结论:
- 单靠轨迹几何学来解释高山滑雪的性能结果是不够的.
- 将可穿戴生物力学与轨迹建模相结合,为诊断技术低效提供了一个实用,可在现场部署的工具.
- 这种综合方法提供了一种低成本,有效的方法来支持高山滑雪训练中的技术优化.
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