The Modeling of Productivity Level and the Comparability of Sport Evaluation Depending on an Athlete’s Age

Portnaia, I. A.; Demakov, V I; Rerke, V. I.; Larionova, Ye. Yu.; Golodkov, Yu. E.

doi:10.1134/s2079057022010118

Cited by 3 publications

(5 citation statements)

References 5 publications

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“…In general, the single hidden layer MLP is a function f: R D ⟶ R L , where D is the dimension of x on the input vector, L is the dimension of the output vector, and the following is the matrix form: (2) + W (2) s b (1) + W (1) x…”

Section: Multilayer Perception Multilayer Perceptron (Mlp)mentioning

confidence: 99%

“…In the above formula, b (1) and b (2) are deviation vectors, the weight vector is represented by W (1) and W (2) , and the activation function is represented by G, and generally, S selects sigmoid function or tanh function as the activation function. It can be regarded as a logistic regression model with many categories from the hidden layer to the output layer, that is, softmax regression.…”

Section: Multilayer Perception Multilayer Perceptron (Mlp)mentioning

confidence: 99%

“…However, at present, the equipment used in the physical fitness evaluation of athletes in China is relatively old, and the physical fitness evaluation information of sports athletes cannot be accurately counted and measured. As a result, coaches do not know the physical fitness of each sports athlete, and they are unable to formulate sports training and competition plans [ 2 ]. This research addresses this issue by designing and developing a physical fitness measurement information system for sports players using artificial intelligence technology.…”

Section: Introductionmentioning

confidence: 99%

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Design of the Physical Fitness Evaluation Information Management System of Sports Athletes Based on Artificial Intelligence

Liu

Zhu

2022

Computational Intelligence and Neuroscience

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With the rapid development of science and technology in recent years, more and more researchers began to explore the basic disciplines of sports, namely, biomechanics and physiology, and further update and improve the traditional sports training theory. However, the developed countries, i.e., Europe and the United States, have dedicatedly worked on the effective role of physical training particularly in middle-class farmers in sports powers. They have realized that the continuous improvement of the physical training evaluation system is directly related to the reforming in the evaluation and monitoring methods of physical training. At the same time, there is a demand for athletes’ physical fitness evaluation, which requires a large amount of data support during the evaluation. These data collection and analysis are difficult and consume a lot of manpower. Therefore, this paper uses artificial intelligence technology to design the sports athletes’ physical fitness evaluation information management system to improve the comprehensiveness of system data collection and the accuracy of data analysis. Through the physical attenuation calculation and physical analysis of the system developed in this paper, data accuracy is high. The coach can make a training plan according to the physical consumption of athletes in sports training or competition, which can greatly improve the ability of athletes.

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Section: Multilayer Perception Multilayer Perceptron (Mlp)mentioning

confidence: 99%

Section: Multilayer Perception Multilayer Perceptron (Mlp)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design of the Physical Fitness Evaluation Information Management System of Sports Athletes Based on Artificial Intelligence

Liu

Zhu

2022

Computational Intelligence and Neuroscience

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“…In the case where the covariance matrix is unknown, the researchers propose an optimal shrinkage mean estimation method by minimizing the quadratic expected loss function [10]. Under the large-dimensional asymptotic framework, related scholars have also deduced an optimal shrinkage estimate of the high-dimensional mean vector by using the random matrix theory [11]. Related scholars also discussed the estimation of the mean vector of the multivariate normal distribution with an unknown singular covariance matrix and obtained a variety of shrinking mean estimation methods [12].…”

Section: Related Workmentioning

confidence: 99%

Evaluation Model of College Physical Education Based on Random Matrix Simulation Algorithm

Liu¹,

Li²

2022

Mathematical Problems in Engineering

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In this study, improved algorithms based on kernel norm minimization are proposed, namely, standard CUR decomposition and fast CUR decomposition. The CUR decomposition algorithm is to decompose the matrix into three parts, namely, C, U, and R. The matrices C and R are sampled by the column selection algorithm, and then, matrix inversion and matrix multiplication are performed to obtain the cross-matrix U. The matrix C ∗ U ∗ R is an approximation of the original matrix. For the improvement of the singular value algorithm, two random algorithms are proposed, namely, the standard random k-SVD algorithm and the fast random k-SVD algorithm. The main idea is to perform dimensionality reduction and random sampling on the original large-scale data matrix, use the random projection algorithm to obtain an approximation of the original data matrix, then, perform corresponding matrix operations on this approximate matrix, and finally obtain a result similar to the original matrix calculation. Based on the empirical investigation of the current situation of physical education teaching evaluation in a university, this research conducts a questionnaire survey on the student group. Combined with the classification of various majors, the relevant factors of the physical education teaching evaluation system are analyzed and studied, and a more reasonable and unique evaluation model is established. We analyze the dimensions and content of the evaluation index system of physical education, and determine the indicators and weights. From the perspective of the combination of physical education curriculum reform and professional characteristics in a university, this study analyzes the current situation of physical education teaching evaluation and builds a sports teaching evaluation system, so as to deepen the reform of physical education teaching in colleges and universities and create a sports education system with higher vocational characteristics. The teaching evaluation model has certain practical significance.

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“…According to the National Physique Monitoring Bulletin, published for the fifth time in 2020 [1], the overall national physical fitness level is improving, however, the excessive growth rate of overweight and obesity is the primary problem of the national physical quality [2,3]. There is extensive literature demonstrating the health benefits of participating in sports, but each sport should assess the physical fitness of the participants [4,5]. If one possesses muscular physical fitness, it can enhance one's athletic performance and help maintain a healthier physical condition.…”

Section: Introductionmentioning

confidence: 99%

Multi-classification of national fitness test grades based on statistical analysis and machine learning

Yang,

Wang,

Cao

et al. 2023

PLoS ONE

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Physical fitness is a key element of a healthy life, and being overweight or lacking physical exercise will lead to health problems. Therefore, assessing an individual’s physical health status from a non-medical, cost-effective perspective is essential. This paper aimed to evaluate the national physical health status through national physical examination data, selecting 12 indicators to divide the physical health status into four levels: excellent, good, pass, and fail. The existing challenge lies in the fact that most literature on physical fitness assessment mainly focuses on the two major groups of sports athletes and school students. Unfortunately, there is no reasonable index system has been constructed. The evaluation method has limitations and cannot be applied to other groups. This paper builds a reasonable health indicator system based on national physical examination data, breaks group restrictions, studies national groups, and hopes to use machine learning models to provide helpful health suggestions for citizens to measure their physical status. We analyzed the significance of the selected indicators through nonparametric tests and exploratory statistical analysis. We used seven machine learning models to obtain the best multi-classification model for the physical fitness test level. Comprehensive research showed that MLP has the best classification effect, with macro-precision reaching 74.4% and micro-precision reaching 72.8%. Furthermore, the recall rates are also above 70%, and the Hamming loss is the smallest, i.e., 0.272. The practical implications of these findings are significant. Individuals can use the classification model to understand their physical fitness level and status, exercise appropriately according to the measurement indicators, and adjust their lifestyle, which is an important aspect of health management.

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The Modeling of Productivity Level and the Comparability of Sport Evaluation Depending on an Athlete’s Age

Cited by 3 publications

References 5 publications

Design of the Physical Fitness Evaluation Information Management System of Sports Athletes Based on Artificial Intelligence

Design of the Physical Fitness Evaluation Information Management System of Sports Athletes Based on Artificial Intelligence

Evaluation Model of College Physical Education Based on Random Matrix Simulation Algorithm

Multi-classification of national fitness test grades based on statistical analysis and machine learning

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