Swimming Training Evaluation Method Based on Convolutional Neural Network
By investigating the status quo of the swimming training market in a certain area, we can obtain information on the current development of the swimming training market in a certain area and study the laws of the development of the market so as to provide a theoretical basis for the development of th...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/4868399 |
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| author | Lei Zhang Wei Liu |
| author_facet | Lei Zhang Wei Liu |
| author_sort | Lei Zhang |
| collection | DOAJ |
| description | By investigating the status quo of the swimming training market in a certain area, we can obtain information on the current development of the swimming training market in a certain area and study the laws of the development of the market so as to provide a theoretical basis for the development of the market. This paper designs an evaluation algorithm suitable for swimming training based on the improved AlexNet network. The algorithm model uses a 3 × 3 size convolution kernel to extract features, and the pooling layer uses a nonoverlapping pooling strategy. In order to accelerate the network convergence, the model introduces batch normalization technology. The algorithm uses data augmentation technology to expand the data set, including rotation and random erasure, to a certain extent alleviating the problem of overfitting. The results of the study showed that there were no significant differences in fat, minerals, protein, body mass index, basal metabolic rate, and total energy expenditure in the body composition ratios of children in the convolutional neural network assessment group and the control group, while muscle and total body water were not significantly different. However, there are significant differences in fat-free body weight and muscle strength of various segments of the body, among which there are very significant differences in muscle strength of lower limbs in each segment of the body. There were no significant differences in minerals, body mass index, basal metabolic rate, total energy expenditure, and lower limb muscle strength in the body composition ratios of men and women in the convolutional neural network assessment group. There are significant differences in body weight, upper limb muscle strength, and trunk muscle strength. There were no significant differences in the proportions of body composition between men and women in the control group, except for fat and protein. |
| format | Article |
| id | doaj-art-22d82d297f44405681ac768d19e4b48f |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-22d82d297f44405681ac768d19e4b48f2025-02-03T00:58:59ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/48683994868399Swimming Training Evaluation Method Based on Convolutional Neural NetworkLei Zhang0Wei Liu1Physical Education Department, Xuzhou Institute of Technology, Jiangsu, Xuzhou 221008, ChinaSchool of Medical Information & Engineering of Xuzhou Medical College, Jiangsu, Xuzhou 221004, ChinaBy investigating the status quo of the swimming training market in a certain area, we can obtain information on the current development of the swimming training market in a certain area and study the laws of the development of the market so as to provide a theoretical basis for the development of the market. This paper designs an evaluation algorithm suitable for swimming training based on the improved AlexNet network. The algorithm model uses a 3 × 3 size convolution kernel to extract features, and the pooling layer uses a nonoverlapping pooling strategy. In order to accelerate the network convergence, the model introduces batch normalization technology. The algorithm uses data augmentation technology to expand the data set, including rotation and random erasure, to a certain extent alleviating the problem of overfitting. The results of the study showed that there were no significant differences in fat, minerals, protein, body mass index, basal metabolic rate, and total energy expenditure in the body composition ratios of children in the convolutional neural network assessment group and the control group, while muscle and total body water were not significantly different. However, there are significant differences in fat-free body weight and muscle strength of various segments of the body, among which there are very significant differences in muscle strength of lower limbs in each segment of the body. There were no significant differences in minerals, body mass index, basal metabolic rate, total energy expenditure, and lower limb muscle strength in the body composition ratios of men and women in the convolutional neural network assessment group. There are significant differences in body weight, upper limb muscle strength, and trunk muscle strength. There were no significant differences in the proportions of body composition between men and women in the control group, except for fat and protein.http://dx.doi.org/10.1155/2021/4868399 |
| spellingShingle | Lei Zhang Wei Liu Swimming Training Evaluation Method Based on Convolutional Neural Network Complexity |
| title | Swimming Training Evaluation Method Based on Convolutional Neural Network |
| title_full | Swimming Training Evaluation Method Based on Convolutional Neural Network |
| title_fullStr | Swimming Training Evaluation Method Based on Convolutional Neural Network |
| title_full_unstemmed | Swimming Training Evaluation Method Based on Convolutional Neural Network |
| title_short | Swimming Training Evaluation Method Based on Convolutional Neural Network |
| title_sort | swimming training evaluation method based on convolutional neural network |
| url | http://dx.doi.org/10.1155/2021/4868399 |
| work_keys_str_mv | AT leizhang swimmingtrainingevaluationmethodbasedonconvolutionalneuralnetwork AT weiliu swimmingtrainingevaluationmethodbasedonconvolutionalneuralnetwork |