Xu Fei, Zhao Bin, and  Yang Huixin. Sports Long-Distance Running Performance Prediction Based on Logistic Regression Model. Dynamic Systems and Applications 29 (2020) No. 5, 1851 – 1858

https://doi.org/10.46719/dsa202029517

ABSTRACT.
This work the show effects at the top are perpendicular to the Ground Quick Reaction Force (GRF), one runs the risk of injury to identify the highlights. This work is to compile the problem by setting a double prediction on GRF control debut. The kinematic information, collected by the Vicon movement structure (Oxford Matrix Collection, UK) was made to be used to point out the importance of various crude signs, especially the scar plane. In this way, prophetic sample information is introduced as a multi-channel time arrangement. In-depth learning techniques have been used in light of a Multi-Layered Perceptron (MLP) assortment of specimens for five specific Convolutional Neural Systems(CNN), Support Vector Machine (SVM), K- Nearest Neighbor calculations. Exhibitions that show Logistic Regression Model Strategic, and Spontaneous Classifiers do not differ fundamentally from many others. The best accuracy of the acquired members is 81.09% . Due to the good execution of the models, the study complements the additional use of in-depth study ways to deal with the intended effect based on this type of information data.

Keywords: Convolutional neural systems, KNN, Multi-Layered Perceptron, Ground Response Force, SVM.