摘要
为探究人工智能三维运动分析应用于分析跳台滑雪飞行距离影响因素和优化运动员技术动作的可行性,在2022年国际雪联洲际杯北京站比赛中对16名运动员的起跳阶段动作进行固定范围拍摄,使用人工智能三维运动分析系统对拍摄视频进行自动三维动作解析。该分析系统可以获取运动员起跳阶段的生物力学参数,通过对比人工与智能系统在处理人体关节点三维坐标时间序列时的相关系数及差值,对该系统应用于跳台滑雪的可行性进行验证,其结果的多重相关系数大于0.91,差值平均值小于1.48 cm。最后对国外高水平运动员和国内运动员技术动作参数进行比较,并采用t检验和皮尔逊相关系数法分析起跳阶段身体姿态参数与运动表现的相关关系。结果显示运动员出台速度与蹬伸开始踝角、膝角相关,最终成绩与蹬伸开始踝角相关,提示中国运动员应注重起跳阶段的充分蹬伸、把握起跳时机,能够显著提高运动表现。可见该系统能够对跳台滑雪技术分析做到精准反馈,且可获得世界冠军运动员生物力学参数以构建冠军模型为中国运动员提供训练参考。
To explore the feasibility of using AI(artificial intelligence)three-dimensional motion analysis for analyzing the influencing factors on the distance of ski jumping and optimizing athletes technical movements,a study was conducted during the 2022 FIS Continental Cup Beijing event.Sixteen athletes take-off phase motions were captured within a fixed range using AI-based three-dimensional motion analysis system.This system automatically parsed the videos to obtain biomechanical parameters of the athletes take-off phase.By comparing the correlation coefficients and differences between manually processed data and AI-generated data of the three-dimensional coordinates of body joints over time,the validation of the equipment for ski jumping was conducted.The multiple correlation coefficient was found to be greater than 0.91,with an average difference value of less than 1.48 cm,indicating the reliability of the system.Furthermore,a comparison was made between the technical parameters of high-level foreign athletes and domestic athletes.Using t-tests and Pearson correlation coefficient analysis,the relationship between body posture parameters during the take-off phase and sports performance was examined.The results reveal correlations between take-off speed and angles of ankle and knee during the take-off phase,and between the final score and ankle angle during take-off,suggesting that Chinese athletes should focus on achieving full extension during take-off and timing their jumps appropriately to significantly enhance sports performance.Overall,the system demonstrated precise feedback for ski jumping technique analysis.Additionally,it enabled the acquisition of biomechanical parameters from world champion athletes to construct a champion model,providing valuable training references for Chinese athletes.
作者
历建宇
梁东雪
曹春梅
张栋
LI Jian-yu;LIANG Dong-xue;CAO Chun-mei;ZHANG Dong(Division of Sports Science and Physical Education,Tsinghua University,Beijing 100084,China;China Institute of Artificial Intelligence in Sports,Capital University of Physical Education and Sports,Beijing 100091,China)
出处
《科学技术与工程》
北大核心
2025年第6期2389-2396,共8页
Science Technology and Engineering
基金
国家重点研发计划“科技冬奥”重点专项(2020YFF0304605)。
关键词
人工智能
深度学习
跳台滑雪
生物力学
artificial intelligence
deep learning
ski jumping
biomechanics
