反向运动
混乱
跳跃
公制(单位)
选择(遗传算法)
计算机科学
心理学
机器学习
运营管理
工程类
物理
量子力学
精神分析
作者
Chris Bishop,Anthony N. Turner,Matthew J. Jordan,John R. Harry,Irineu Loturco,Jason P. Lake,Paul Comfort
标识
DOI:10.1519/ssc.0000000000000677
摘要
ABSTRACT Researchers and practitioners have highlighted the necessity to monitor jump strategy metrics and the commonly reported outcome measures during the countermovement jump (CMJ) and drop jump (DJ) tests. However, there is a risk of confusion for practitioners, given the vast range of metrics that now seem to be on offer via analysis software when collecting data from force platforms. As such, practitioners may benefit from a framework that can help guide metric selection for commonly used jump tests, which is the primary purpose of this article. To contextualize the proposed framework, we have provided 2 examples for how this could work: one for the CMJ and one for the DJ, noting that these tests are commonly used by practitioners during routine testing across a range of sport performance and clinical settings.
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