Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, United Kingdom.
Department of Exercise and Sports Science, The University of Sydney, Lidcombe, Australia; Performance People & Teams, Australian Institute of Sport, Canberra, Australia.
J Sci Med Sport. 2019 Dec;22(12):1355-1360. doi: 10.1016/j.jsams.2019.07.006. Epub 2019 Jul 19.
To support future developments of field-based biomechanical load monitoring tools, this study aimed to identify generalised segmental acceleration patterns and their contribution to ground reaction forces (GRFs) across different running tasks.
Exploratory experimental design.
A multivariate principal component analysis (PCA) was applied to a combination of segmental acceleration data from all body segments for 15 team-sport athletes performing accelerated, decelerated and constant low-, moderate- and high-speed running, and 90° cutting trials. Segmental acceleration profiles were then reconstructed from each principal component (PC) and used to calculate their specific GRF contributions.
The first PC explained 48.57% of the acceleration variability for all body segments and was primarily related to the between-task differences in the overall magnitude of the GRF impulse. Magnitude and timing of high-frequency acceleration and GRF features (i.e. impact related characteristics) were primarily explained by the second PC (12.43%) and also revealed important between-task differences. The most important GRF characteristics were explained by the first five PCs, while PCs beyond that primarily contained small contributions to the overall GRF impulse.
These findings show that a multivariate PCA approach can reveal generalised acceleration patterns and specific segmental contributions to GRF features, but their relative importance for different running activities are task dependent. Using segmental acceleration to assess whole-body biomechanical loading generically across various movements may thus require task identification algorithms and/or advanced sensor or data fusion approaches.
为了支持基于现场的生物力学负荷监测工具的未来发展，本研究旨在确定通用的节段加速度模式及其对不同跑步任务中的地面反作用力（GRF）的贡献。
探索性实验设计。
对 15 名团队运动运动员在加速、减速和恒低速、中速和高速跑步以及 90°变向试验中所有身体节段的节段加速度数据进行多元主成分分析（PCA）。然后从每个主成分（PC）重建节段加速度曲线，并用于计算其特定的 GRF 贡献。
第一主成分解释了所有身体节段加速度变化的 48.57%，主要与 GRF 冲量的整体大小在不同任务之间的差异有关。高频加速度和 GRF 特征（即与冲击相关的特征）的大小和时间主要由第二主成分（12.43%）解释，并且也揭示了重要的任务间差异。最重要的 GRF 特征由前五个主成分解释，而超过这些的主成分主要包含对整体 GRF 冲量的较小贡献。
这些发现表明，多元 PCA 方法可以揭示通用的加速度模式和特定的节段对 GRF 特征的贡献，但它们对不同跑步活动的相对重要性取决于任务。因此，使用节段加速度来普遍评估各种运动中的全身生物力学负荷可能需要任务识别算法和/或先进的传感器或数据融合方法。
