[Retracted] Swimming Training Evaluation Method Based on Convolutional Neural Network

Abstract

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.

Construction Principles of Physical Fitness Training Objective System Based on Machine Learning and Data Mining

“…e results show that nearly half of the students' weight does not meet the standard, and through the operation results of the algorithm, it is observed that the students lack the training of lower limb strength in physical training, and the vital capacity grade and endurance grade are obviously weak. It is suggested that the students strengthen the training of aerobic exercise [10]. Zhang et al screened five strong association rules for boys and girls by using the association rule Apriori algorithm based on the body test data of college students.…”

Research on Scientific Theory and Method of Swimming Sports Training

“…Nowadays, the recovery training for swimmers has become an indispensable part of the training, among which hyperthermia, massage, static stretching and related drug recovery are the most common means of recovery training. In addition, in the recent two years, the continuous improvement of psychological recovery training, nutritionist recovery and adjustment are effective means of recovery training [7].…”

Swimming Movement Guidance Training System Based on OpenCL Technology

“…The re projection error considers not only the calculation error of homography matrix, but also the measurement error of image points. In the homography transformation from the object plane to the image plane as shown in Figure 2, x is a point in the object plane, x′ is the corresponding point of x in the image plane, and the relationship between them should meets the relationship shown in the following equation [14].…”