Research on basketball footwork recognition based on a convolutional neural network algorithm

Objective: The purpose of this paper is to utilize a convolutional neural network (CNN) to identify the types of basketball footwork of athletes as a way to assist in the training of basketball players' footwork and to improve their performance in the game. Methods: A traditional CNN algorithm was improved to a dual-model CNN (DMCNN) algorithm, where convolutional feature extraction was performed separately on both the acceleration and angular velocity data of footwork. The two features were then merged and subjected to principle component analysis (PCA) dimensionality reduction for identifying different types of footwork. In subsequent simulation experiments, ten basketball players' footwork data were collected using sensors. The improved CNN algorithm was used for footwork recognition and compared with the support vector machine (SVM) and traditional CNN algorithms. Results: The experimental results showed that the acceleration and angular velocity signals of different basketball footwork had distinct differences. The comprehensive recognition precision of DMCNN for footwork types was 98.8 %, and the comprehensive recall rate and overall F value were 97.8 % and 98.2 %, respectively. Its recognition time was 1.23 s. For the traditional CNN algorithm, the comprehensive precision was 87.5 %, the comprehensive recall rate was 85.7 %, and the overall F value was 86.6 %. Its recognition time was 1.99 s. As for the SVM algorithm, the comprehensive precision was 74.2 %, the comprehensive recall rate was 73.2 %, and the overall F value was 73.7 %. The recognition time was 3.68 s. Novelty: The novelty of this article lies in using two separate CNNs to extract convolutional features from acceleration and angular velocity, respectively. These features are then combined and reduced dimensionality using PCA, thereby improving both recognition accuracy and efficiency.