Research on the Application of Virtual Technology-based Posture Detection Device in Swimming Teaching

Abstract

With the socio-economic development, the national demand for playing leisure sports has increased, and swimming is one of the popular choices. To help swimming beginners understand the correct swimming posture more quickly and directly, hybrid neural network algorithms based on sliding window detection and deep residual networks are designed in this study, and two corresponding virtual image classification models of swimmer’s posture are designed based on these algorithms. In order to reduce the noise of the input data and reduce the cost of data collection, the virtual reality technology is used to convert the swimmer’s swimming pose image into the image model in the virtual reality space as the input data of the algorithm. The performance test experimental results show that the classification accuracy of the swimmer pose recognition models based on PTP-CNN algorithm and SW-CNN algorithm designed in this research are 97.48% and 96.72% respectively on the test set, which are much higher than other comparison models, and the model built based on PTP-CNN algorithm has the fastest computation speed. The results of this research can be applied to assist participants in swimming pose recognition in teaching beginner swimmers.