■尽管越来越多的证据突出了运动技巧的个性,运动学习的主要模型,特别是在收购阶段,继续强调泛化,独立于人的方法。生物力学研究,再加上机器学习方法,已经证明了个人表现出的运动技术的独特性。然而,这些证据主要与已经稳定的运动技术有关,在周期性的日常活动中尤其明显,例如步行,跑步,或者骑自行车,以及专家级的运动。这项研究旨在评估全身运动中的个性假设,这需要在习得阶段开始时新手参与者之间进行复杂的协调和力量。
■在主题内设计中,16名高度活跃的男性参与者(平均年龄:23.1±2.1岁),学习任务中的所有绝对新手(即,奥运会举重的力量抓举),参加了随机抓举学习比赛。这些回合包括跨各种运动学习模型的36项试验:差异学习情境干扰(serial,sCIL;和阻塞,bCIL),重复学习。从每个运动学习模型回合后进行的三个标准化抓举试验中收集了运动学和动力学数据。将时间连续数据输入到线性支持向量机(SVM)。我们对两个分类任务进行了分析:参与者和运动学习模型。
■与运动学习模型分类相比,支持向量机分类显示出明显优于参与者的分类,平均预测准确率为78%(在跨褶皱的45个测试试验中平均约35个)和27.3%(在跨褶皱的36个测试试验中平均约9个)。在特定的折叠和输入组合中,准确率分别为91%和38%。
■方法,在未来研究的背景下,讨论了选择合适的数据预处理方法和确定SVM数据输入的最佳组合的关键作用。我们的发现为在奥运会举重力量抓举的早期阶段,在运动技术中运动学习模型的个性优势提供了初步支持。
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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) versus 27.3% (in average ≈9 out of 36 test trials across the folds). In specific fold and input combinations, accuracies of 91% versus 38% were respectively achieved.
UNASSIGNED: 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.