Diversified Teaching Strategies for College Swimming Courses in the Context of Artificial Intelligence

Abstract

Swimming is a sport with a very significant exercise effect, so swimming courses in colleges and universities can promote the overall development of students’ physical fitness. In this paper, students’ swimming data are collected using intelligent sensors, and the collected data are noise-reduced and normalized by a low-pass filter. After the completion of data preprocessing, the swimming posture data features are extracted. The features are dimensionality reduced by the PCA method, combined with the BP neural network for training and accurate swimming posture recognition, and on this basis, a diversified teaching system for swimming courses in smart colleges and universities is constructed. After the students’ swimming data set was collected, the recognition effect of each swimming stroke was analyzed, and a comparison experiment was set up to investigate the practical effect and strategy of this teaching mode. The results show that the average accuracy of recognizing each stroke in this study can reach more than 0.988, and in the teaching test, the average difference between the performance of the experimental group and the control group is 7.6, and the P-value of the technical evaluation performance of the two groups of students in this project is 0.002<0.05, which significantly improves the swimming performance. This study can be combined with artificial intelligence technology to diversify the teaching of swimming courses in colleges and universities.

Keywords

• intelligent sensor
• feature extraction
• pca method
• bp neural network
• stroke recognition
• swimming course
• 68t01