Ammar Achraf, Simak Marvin Leonard, Salem Atef, Horst Fabian, Schöllhorn Wolfgang Immanuel
Department of Training and Movement Science, Institute of Sport Science, Johannes Gutenberg-University Mainz, Mainz, Germany.
Research Laboratory, Molecular Bases of Human Pathology, LR19ES13, Faculty of Medicine of Sfax, University of Sfax, Sfax, Tunisia.
Front Bioeng Biotechnol. 2024 Jul 30;12:1426058. doi: 10.3389/fbioe.2024.1426058. eCollection 2024.
Despite the growing body of evidence highlighting the individuality in movement techniques, predominant models of motor learning, particularly during the acquisition phase, continue to emphasise generalised, person-independent approaches. Biomechanical studies, coupled with machine learning approaches, have demonstrated the uniqueness of movement techniques exhibited by individuals. However, this evidence predominantly pertains to already stabilised movement techniques, particularly evident in cyclic daily activities such as walking, running, or cycling, as well as in expert-level sports movements. This study aims to evaluate the hypothesis of individuality in whole-body movements necessitating intricate coordination and strength among novice participants at the very beginning of an acquisition phase.
In a within-subject design, sixteen highly active male participants (mean age: 23.1 ± 2.1 years), all absolute novices in the learning task (i.e., power snatch of Olympic weightlifting), participated in randomised snatch learning bouts. These bouts comprised 36 trials across various motor learning models: differential learning contextual interference (serial, sCIL; and blocked, bCIL), and repetitive learning. Kinematic and kinetic data were collected from three standardised snatch trials performed following each motor learning model bout. The time-continuous data were input to a linear Support Vector Machine (SVM). We conducted analyses on two classification tasks: participant and motor learning model.
The Support Vector Machine classification revealed a notably superior participant classification compared to the motor learning model classification, with an averaged prediction accuracy of 78% (in average ≈35 out of 45 test trials across the folds) 27.3% (in average ≈9 out of 36 test trials across the folds). In specific fold and input combinations, accuracies of 91% 38% were respectively achieved.
Methodically, the crucial role of selecting appropriate data pre-processing methods and identifying the optimal combinations of SVM data inputs is discussed in the context of future research. Our findings provide initial support for a dominance of individuality over motor learning models in movement techniques during the early phase of acquisition in Olympic weightlifting power snatch.
尽管越来越多的证据凸显了运动技术中的个体差异，但主要的运动学习模型，尤其是在习得阶段，仍继续强调通用的、不依赖个体的方法。生物力学研究与机器学习方法相结合，已证明个体所展现的运动技术具有独特性。然而，这一证据主要涉及已经稳定的运动技术，在诸如行走、跑步或骑自行车等日常周期性活动以及专家级别的体育动作中尤为明显。本研究旨在评估在习得阶段刚开始时，新手参与者进行需要复杂协调和力量的全身运动中个体差异这一假设。
在一项受试者内设计中，16名高度活跃的男性参与者（平均年龄：23.1±2.1岁），他们在学习任务（即奥运举重的力量抓举）中均为绝对新手，参与了随机抓举学习回合。这些回合包括针对各种运动学习模型的36次试验：差异学习情境干扰（序列式，sCIL；和分组式，bCIL）以及重复学习。在每个运动学习模型回合之后进行的三次标准化抓举试验中收集运动学和动力学数据。将时间连续数据输入线性支持向量机（SVM）。我们对两项分类任务进行了分析：参与者和运动学习模型。
支持向量机分类显示，与运动学习模型分类相比，参与者分类明显更优，平均预测准确率为78%（平均约为各折45次测试试验中的35次），而运动学习模型分类的准确率为27.3%（平均约为各折36次测试试验中的9次）。在特定的折和输入组合中，准确率分别达到了91%和38%。
在方法上，在未来研究的背景下讨论了选择合适的数据预处理方法以及确定支持向量机数据输入的最佳组合的关键作用。我们的研究结果为在奥运举重力量抓举习得早期阶段，个体差异在运动技术中比运动学习模型占主导地位提供了初步支持。
