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标题：Linear and Nonlinear Prediction Models Show Comparable Precision for Maximal Mean Speed in a 4x1000 m Field Test
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作者：J. M. Jäger ; J. Kurz ; H. Müller 等
期刊名称：International Journal of Computer Science in Sport
电子版ISSN：1684-4769
出版年度：2017
卷号：16
期号：2
页码：78-87
DOI：10.1515/ijcss-2017-0007
语种：English
出版社：Sciendo
摘要：Abstract Maximal oxygen uptake (VO2max) is one of the most distinguished parameters in endurance sports and plays an important role, for instance, in predicting endurance performance. Different models have been used to estimate VO2max or performance based on VO2max. These models can use linear or nonlinear approaches for modeling endurance performance. The aim of this study was to estimate VO2max in healthy adults based on the Queens College Step Test (QCST) as well as the Shuttle Run Test (SRT) and to use these values for linear and nonlinear models in order to predict the performance in a maximal 1000 m run (i.e. the speed in an incremental 4×1000 m Field Test (FT)). 53 female subjects participated in these three tests (QCST, SRT, FT). Maximal oxygen uptake values from QCST and SRT were used as (a) predictor variables in a multiple linear regression (MLR) model and as (b) input variables in a multilayer perceptron (MLP) after scaling in preprocessing. Model output was speed [km·h−1] in a maximal 1000 m run. Maximal oxygen uptake values estimated from QCST (40.8 ± 3.5 ml·kg−1·min−1) and SRT (46.7 ± 4.5 ml·kg−1·min−1) were significantly correlated (r = 0.38, p < 0.01) and maximal mean speed in the FT was 12.8 ± 1.6 km·h−1. Root mean squared error (RMSE) of the cross validated MLR model was 0.89 km·h−1 while it was 0.95 km·h−1 for MLP. Results showed that the accuracy of the applied MLP was comparable to the MLR, but did not outperform the linear approach.
关键词：Keywordsmultiple linear regressionmultilayer perceptronperformance predictionartificial neural network