Temporal changes in ground reaction force magnitudes reflect movement strategy, and thus underlying muscle activation patterns, during movement tasks. Speculatively, these changes may be observed more readily when the force-time signal is differentiated, yielding the yank-time signal. However, the differentiation process, including the signal filtering used before or after differentiation, can significantly affect the signal-to-noise ratio (SNR) and likelihood of meaningful inference. The aim of the present study was to compare three methods of deriving the yank-time signal: Method 1 derived the yank-time signal using 2^nd-order central differentiation subsequent to application of a 4^th-order Butterworth filter; Method 2 included the same process as Method 1 but additionally filtered the yank-time data with a Savitzky-Golay smoothing filter; and Method 3 directly and simultaneously derived and smoothed the yank-time signal using a Savitzky-Golay digital differentiation filter. The current analyses revealed Method 2 had the best SNR, followed by Method 3 and 1, but caused a small loss of signal amplitude. With regards to timing of inflection points in the yank-time data, no significant difference was observed. Therefore, Method 3 led to the best derivation of the yank-time signal due to its efficiency and preservation of signal characteristics and good SNR. Also, a strong association between the first maximum point of the yank-time signal and the start of the downward movement of the body’s centre of mass during a countermovement jump, as identified by 3-D motion analysis, was observed. Thus, subtle events (e.g. start of downward movement) can be easily observed in the yank-time signal.

地面反作用力大小​​的时间变化反映了运动任务期间的运动策略，从而反映了潜在的肌肉激活模式。推测地，当对力时间信号进行微分时，可以更容易地观察到这些变化，从而产生拉动时间信号。但是，微分过程（包括微分之前或之后使用的信号过滤）会显着影响信噪比（SNR）和有意义推断的可能性。本研究的目的是比较导出抽出时间信号的三种方法：方法1衍生使用2抽出时间信号^ND阶中央分化之后4的应用^日阶巴特沃斯滤波器 方法2包含与方法1相同的过程，但另外使用了Savitzky-Golay平滑滤波器对抽检时间数据进行了滤波；以及方法3使用Savitzky-Golay数字微分滤波器直接，同时导出并平滑抽动时间信号。当前的分析表明，方法2具有最佳的SNR，其次是方法3和1，但造成信号幅度的损失很小。关于拉动时间数据中拐点的时间，未观察到显着差异。因此，方法3由于其效率高，信号特性和良好的SNR得以保留，从而导致了最佳的推挽时间信号推导。也，通过3-D运动分析可以确定，猛拉时间信号的第一个最大值与反向运动跳跃期间身体质心的向下运动的开始之间存在很强的关联。因此，在拉动时间信号中可以轻松观察到细微的事件（例如，开始向下运动）。