Applying Deep Learning Networks to Identify Optimized Paths in Gymnastic Movement Techniques

The study adopts the OpenPose algorithm in deep learning to extract and recognize gymnastics movements, and it initially constructs the OpenPose gymnastics movement recognition model. The MobileNet-V3 network is introduced to replace VGG-19, which was the feature extraction network in the original model, in order to optimize the accuracy of OpenPose in recognizing gymnastics actions and to construct an OpenPose-MobileNet-V3 gymnastics action recognition model. The original model is compared with the optimized OpenPose-MobileNet-V3 model for comparison experiments in action recognition, and then the OpenPose-MobileNet-V3 model is compared with other recognition models to examine its effectiveness in action recognition. Finally, the parameter sensitivities of MobileNet-V3 and cosine annealing strategies are compared to explore the optimization effect of the two strategies on the OpenPose model.The OpenPose-MobileNet-V3 algorithm improves its recognition accuracy by 6.857% over the pre-optimization OpenPose algorithm.The recognition accuracy of the OpenPose-MobileNet-V3 is improved by 6.857% on the two datasets, which have accuracies of 95.786% and 94.572%, respectively, which are significantly better than other recognition models. The cosine annealing strategy-trained model is 2.143 percentage points less accurate than the OpenPose-MobileNet-V3 model at recognizing gymnastics movements, and MobileNet-V3 is better optimized.