A deep learning-based action recognition and confrontation analysis system for sparring players

There is an increasing demand for assisted training techniques in the sport of sparring. In this paper, a sparring multiple recognition and analysis system is designed and fabricated for the movements of sparring sports and used to recognize and analyze the players’ technical movements using the collected data and the model built using deep neural networks. The CNN-LSTM network is applied to extract the feature classification of the preprocessed sparring inertia data, and then the DTW algorithm is combined with the spatial distance classification method to realize the matching and recognition of sparring behaviors by stretching and compressing transformations of the time axis, effectively eliminating the distortion error in the time domain and obtaining the similar path with the shortest cumulative distance of the effective matches between different sequences. Experiments on the application of this paper’s system were conducted in two groups of sparring players, and after 12 weeks of training intervention, the average confrontation striking speed of the experimental group progressed from 0.36 seconds before the experiment to 0.32 seconds after the experiment, and the average performance of the control group progressed from 0.38 seconds before the experiment to 0.36 seconds after the experiment, which indicates that although the traditional resistance training also has a positive impact on the training effect of sparring training, the training effect of this paper’s system is more obvious The systematic training effect of this paper is more obvious. This paper makes an innovative exploration for the combination of sports programs such as sparring and cutting-edge information technology.