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基于机器学习算法的杠铃高悬垂位实力抓举与借力单杠双力臂动作的拮抗关系研究

Study on the antagonistic relationship between barbell high-hang muscle snatch and kipping bar muscle-up based on machine learning algorithms
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摘要 基于借力单杠双力臂动作中的身体姿势变化设计其拮抗训练动作——杠铃高悬垂位实力抓举,并使用机器学习算法验证与量化不同重量的杠铃高悬垂位实力抓举与借力单杠双力臂动作的拮抗关系。招募有经验的综合体能训练爱好者10名,通过线性传感器计与惯性测量单元采集借力单杠双力臂动作和杠铃高悬垂位实力抓举动作的运动生物力学数据,使用一维统计参数映射(SPM1D)和动态时间规整(DTW)机器学习算法,验证与量化两者之间的拮抗关系。结果显示:两个动作在肩关节外展内收、内旋外旋、屈曲伸展以及肘关节内旋外旋上,存在拮抗关系。当杠铃重量约60%1RM(1次最大重量)时,两个动作的动力学数据拟合度最佳;当杠铃重量低于60%1RM时,两个动作的关节运动的拮抗关系更加显著。研究认为:杠铃高悬垂位实力抓举动作与借力单杠双力臂动作存在拮抗关系,可用其来优化借力单杠双力臂动作的训练效果,但杠铃配重需要根据训练目标和训练者能力进行调整。当训练目标为提升上肢爆发力时,可使用约60%1RM的杠铃配重,而当训练目标为提升借力单杠双力臂动作技巧时,则可用低于60%1RM的杠铃配重。 This study aimed to design an antagonistic training movement,which was barbell high-hang muscle snatch for the kipping bar muscle-up based on changes in body posture,and then machine learning algorithms were also employed to validate and quantify the antagonistic relationship between these exercises under various loads.10 young and experienced fitness enthusiasts were recruited to participate in this study.Biomechanics parameters for kipping bar muscle-up and barbell high-hang muscle snatch were collected by using linear sensors and inertial measurement units,and then one-dimensional statistical parametric mapping(SPM1D)and dynamic time warping(DTW)algorithms were applied to verify and quantify the antagonistic relationship between the two movements.The results showed that an antagonistic relationship was confirmed between the two movements in terms of shoulder joint abduction and adduction,internal and external rotation,flexion and extension,as well as elbow joint internal and external rotation.Optimal dynamic parameters fitting occurred at a barbell weight of approximately 60%1RM(one-repetition maximum),while the most pronounced antagonistic kinematic relationship was observed at weights below 60%1RM.The conclusion indicated that the barbell high-hang muscle snatch exhibits an antagonistic relationship to the kipping bar muscle-up and can be effectively used to optimize its training effect,but the load must be adjusted based on the objectives of training and individual capabilities.For enhancing upper limb explosive strength,a load around 60%1RM will be recommended,whereas for improving skill of kipping bar muscle-up,lighter loads below 60%1RM are advised.
作者 石智勇 徐异宁 顾耀东 SHI Zhiyong;XU Yining;GU Yaodong(Faculty of Sports Science,Ningbo University,Ningbo 315211,China)
出处 《体育学刊》 CAS CSSCI 北大核心 2024年第6期140-148,共9页 Journal of Physical Education
基金 宁波市重大科技任务攻关项目科技创新2025重大专项(2022Z196) 浙江省哲学社会科学规划领军人才培育课题青年英才培育(22QNYC10ZD)。
关键词 运动生物力学 拮抗动作 运动训练 杠铃高悬垂位实力抓举 借力单杠双力臂动作 机器学习算法 sports biomechanics antagonistic movement sports training barbell high-hang muscle snatch kipping bar muscle-up machine learning algorithms
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