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What is the best fitting function? Evaluation of lactate curves with common methods from the literature

机译:什么是最好的拟合功能?用文献中的常用方法评估乳酸曲线

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Using the lactate threshold for training prescription is the gold-standard, although there are several open questions. One open question is: What is the best fitting method for the load-lactate data points? This investigation re-analyses over 3500 lactate diagnostic datasets in swimming. Our evaluation software examines six different fitting methods with two different minimization criteria (RMSE and SE). Optimization of parameters of the functions is put in excecution with gradient descent. From a mathematical point of view, the double phase model, which consists of two linear regression lines, shows the least errors (RMSE_(min) 0.254 ± 0.172; SE_(min) 0.311 ± 0.210). However, this method cannot be used for every further determination of lactate thresholds. Some threshold determination models need a single curve. In these cases, the exponential function shows the least errors (RMSE_(min) 0.846 ± 0.488; SE_(min) 1.196 ± 0.689). This confirms the default fitting method used in practice.
机译:使用训练处方的乳酸阈值是金标准,尽管有几个开放性问题。一个打开的问题是:负载乳酸数据点的最佳拟合方法是什么?该研究在游泳中重新分析了3500多个乳液诊断数据集。我们的评估软件审查了六种不同的拟合方法,具有两个不同的最小化标准(RMSE和SE)。使用梯度下降,将函数参数的优化进行了反应。从数学的角度来看,由两个线性回归线组成的双相模型表示误差最小(RMSE_(min)0.254±0.172; SE_(min)0.311±0.210)。然而,这种方法不能用于每种进一步确定乳酸阈值。一些阈值确定模型需要单个曲线。在这些情况下,指数函数显示误差最小(RMSE_(min)0.846±0.488; SE_(min)1.196±0.689)。这证实了实践中使用的默认拟合方法。

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