Burdack Johannes, Horst Fabian, Aragonés Daniel, Eekhoff Alexander, Schöllhorn Wolfgang Immanuel
Department of Training and Movement Science, Institute of Sports Science, Johannes Gutenberg University Mainz, Mainz, Germany.
Department of Wushu, School of Martial Arts, Shanghai University of Sport, Shanghai, China.
Front Psychol. 2020 Sep 30;11:551548. doi: 10.3389/fpsyg.2020.551548. eCollection 2020.
The scientific and practical fields-especially high-performance sports-increasingly request a stronger focus be placed on individual athletes in human movement science research. Machine learning methods have shown efficacy in this context by identifying the unique movement patterns of individuals and distinguishing their intra-individual changes over time. The objective of this investigation is to analyze biomechanically described movement patterns during the fatigue-related accumulation process within a single training session of a high number of repeated executions of a ballistic sports movement-specifically, the frontal foot kick () in karate-in expert athletes. The two leading research questions presented for consideration are (1) Can characteristics of individual movement patterns be observed throughout the entire training session despite continuous changes, i.e., even as fatigue-related processes increase? and (2) How do intra-individual movement patterns change as fatigue-related processes increase throughout a training session? Sixteen expert karatekas performed 606 frontal foot kicks directed toward an imaginary target. The kicks were performed in nine sets at 80% (-80) of the self-experienced maximal intensity. In addition, six kicks at maximal intensity (-100) were performed after each of the nine sets. Between the sets, the participants took a 90-s break. Three-dimensional full-body kinematic data of all kicks were recorded with 10 infrared cameras. The normalized waveforms of nine upper- and lower-body joint angles were classified using a supervised machine learning method (support vector machine). The results of the classification revealed a disjunct distinction between the kinematic movement patterns of individual athletes. The identification of unique movement patterns of individual athletes was independent of the intensity and the degree of fatigue-related processes. In other words, even with the accumulation of fatigue-related processes, the unique movement patterns of an individual athlete can be clearly identified. During the training session, changes in intra-individual movement patterns could also be detected, indicating the occurrence of adaptations in individual movement patterns throughout the fatigue-related accumulation process. The results suggest that these adaptations can be modeled in terms of changes in patterns rather than increasing variance. Practical consequences are critically discussed.
在科学和实践领域，尤其是在高性能运动领域，人类运动科学研究越来越需要更加关注个体运动员。机器学习方法在这方面已显示出功效，它能够识别个体独特的运动模式，并区分其随时间的个体内部变化。本研究的目的是分析在大量重复执行弹道式体育动作（具体而言，空手道中的前足踢（ ））的单次训练中，与疲劳相关的积累过程中通过生物力学描述的运动模式。提出供考虑的两个主要研究问题是：（1）尽管存在持续变化，即在与疲劳相关的过程增加时，在整个训练过程中是否能观察到个体运动模式的特征？（2）在整个训练过程中，随着与疲劳相关的过程增加，个体内部运动模式如何变化？16名空手道专家针对假想目标进行了606次前足踢。这些踢腿以九组进行，每组强度为自我体验到的最大强度的80%（-80）。此外，在每组九次踢腿之后，还以最大强度（-100）进行了六次踢腿。两组之间，参与者休息90秒。使用10台红外摄像机记录所有踢腿的三维全身运动学数据。采用监督机器学习方法（支持向量机）对九个上半身和下半身关节角度的归一化波形进行分类。分类结果揭示了个体运动员运动学运动模式之间的明显区别。个体运动员独特运动模式的识别与强度和与疲劳相关的过程程度无关。换句话说，即使与疲劳相关的过程在积累，个体运动员的独特运动模式也能被清晰识别。在训练过程中，还能检测到个体内部运动模式的变化，这表明在整个与疲劳相关的积累过程中个体运动模式发生了适应性变化。结果表明，这些适应性变化可以用模式变化而非方差增加来建模。文中对实际应用后果进行了批判性讨论。
