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采用单试验运动等效性分析来评估运动学习。

Employing a single trial motor equivalent analysis for the assessment of motor learning.

作者信息

Beerse Matthew, Bigelow Kimberly E, Barrios Joaquin A

机构信息

Department of Health and Sport Science, University of Dayton, 300 College Park, Dayton, OH, 45469-2968, USA.

Department of Mechanical and Aerospace Engineering, University of Dayton, Dayton, OH, USA.

出版信息

Exp Brain Res. 2025 Jun 20;243(7):179. doi: 10.1007/s00221-025-07123-7.

Abstract

The uncontrolled manifold analysis (UCM) is a useful technique for motor learning research enabling the classification of movement variability into solutions and errors. Less explored methodological considerations within the UCM framework are the selection of mean configurations outside of the current performance, as found in the Motor Equivalence Analysis, and a single trial approach. In this study, we demonstrated how calculating deviations away from varying mean configurations within the UCM influences the results and interpretations within motor learning experiments. Twelve young adult subjects (9F/3 M, 20.53 ± 1.25 years old) practiced the kettlebell swing over a one-week time period. We compared deviations from the mean configuration across all repetitions, to the mean of the first ten repetitions before practice and to the mean of their last ten repetitions after practice. Results suggested that subjects abandoned their initial mean performance within the first sets of kettlebell swings and reduced their errors and solutions towards what would become their mean performance after practice. They continued to refine their performance 1 week later. Subjects then completed a transfer task, testing their ability to adapt to a water-filled kettlebell. We evaluated deviations from their mean performance with the metal kettlebell and their mean performance with the water-filled kettlebell. Subjects did not reduce errors towards their mean metal kettlebell performance, but instead towards a new performance that matched the dynamics of the water-filled kettlebell. When performance is expected to change, i.e., motor learning, assessing how the variance structure changes with respect to different mean configurations can provide further insight when using a UCM approach.

摘要

非控制流形分析(UCM)是运动学习研究中的一种有用技术,可将运动变异性分类为解决方案和误差。在UCM框架内较少被探索的方法学考虑因素包括在当前表现之外选择平均构型(如在运动等效性分析中发现的)以及单试验方法。在本研究中,我们展示了在UCM中计算偏离不同平均构型的偏差如何影响运动学习实验的结果和解释。12名年轻成年受试者(9名女性/3名男性,20.53±1.25岁)在一周时间内练习壶铃摆动。我们比较了所有重复动作与平均构型的偏差、练习前前十个重复动作的平均值以及练习后最后十个重复动作的平均值。结果表明,受试者在最初几组壶铃摆动中放弃了他们最初的平均表现,并朝着练习后将成为他们平均表现的方向减少了误差和解决方案。他们在1周后继续完善自己的表现。然后,受试者完成了一项转移任务,测试他们适应装满水的壶铃的能力。我们评估了他们使用金属壶铃时与平均表现的偏差以及使用装满水的壶铃时的平均表现。受试者没有朝着他们使用金属壶铃的平均表现减少误差,而是朝着与装满水的壶铃动力学相匹配的新表现减少误差。当预期表现会发生变化时,即运动学习,在使用UCM方法时评估方差结构如何相对于不同的平均构型变化可以提供进一步的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0fcc/12181205/64bd7570d2b5/221_2025_7123_Fig1a_HTML.jpg

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