Zago Matteo, Codari Marina, Iaia F Marcello, Sforza Chiarella
a Department of Biomedical Sciences for Health , Università degli Studi di Milano , Milano , Italy.
b Institute of Molecular Bioimaging and Physiology , National Research Council , Segrate , Italy.
J Sports Sci. 2017 Aug;35(15):1515-1522. doi: 10.1080/02640414.2016.1223332. Epub 2016 Aug 25.
Karate is a martial art that partly depends on subjective scoring of complex movements. Principal component analysis (PCA)-based methods can identify the fundamental synergies (principal movements) of motor system, providing a quantitative global analysis of technique. In this study, we aimed at describing the fundamental multi-joint synergies of a karate performance, under the hypothesis that the latter are skilldependent; estimate karateka's experience level, expressed as years of practice. A motion capture system recorded traditional karate techniques of 10 professional and amateur karateka. At any time point, the 3D-coordinates of body markers produced posture vectors that were normalised, concatenated from all karateka and submitted to a first PCA. Five principal movements described both gross movement synergies and individual differences. A second PCA followed by linear regression estimated the years of practice using principal movements (eigenpostures and weighting curves) and centre of mass kinematics (error: 3.71 years; R2 = 0.91, P ≪ 0.001). Principal movements and eigenpostures varied among different karateka and as functions of experience. This approach provides a framework to develop visual tools for the analysis of motor synergies in karate, allowing to detect the multi-joint motor patterns that should be restored after an injury, or to be specifically trained to increase performance.
空手道是一种武术,部分依赖于对复杂动作的主观评分。基于主成分分析(PCA)的方法可以识别运动系统的基本协同作用(主要动作),从而对技术进行定量的全局分析。在本研究中,我们旨在描述空手道表演的基本多关节协同作用,假设后者与技能相关;估计空手道练习者的经验水平,以练习年限表示。一个动作捕捉系统记录了10名职业和业余空手道练习者的传统空手道技术。在任何时间点,身体标记的三维坐标生成姿势向量,这些向量经过归一化处理,将所有空手道练习者的向量连接起来并进行第一次主成分分析。五个主要动作描述了总体动作协同作用和个体差异。第二次主成分分析后进行线性回归,使用主要动作(特征姿势和加权曲线)和质心运动学来估计练习年限(误差:3.71年;R2 = 0.91,P≪0.001)。不同空手道练习者之间以及作为经验的函数,主要动作和特征姿势各不相同。这种方法提供了一个框架,用于开发视觉工具来分析空手道中的运动协同作用,从而能够检测受伤后应恢复的多关节运动模式,或者为提高表现而进行专门训练。