Tennis teaching assistance model based on double chain shared unsupervised action recognition algorithm

• Published in: International journal of cognitive computing in engineering 2025-12, Vol.6, p.21-31
• Main Author: Bai, Hongzhou
• Format: Article
• Language: English
• Subjects: Action recognition; Data augmentation; Human skeleton; Positive sample expansion; Unsupervised learning
• ISSN: 2666-3074; 2666-3074
• DOI: 10.1016/j.ijcce.2024.10.001

•A novel double chain shared unsupervised action recognition algorithm is proposed for tennis teaching, enhancing both training quality and accuracy.•The algorithm integrates a dual chain shared structure, skeleton topology data augmentation, and improved positive sample expansion strategy to achieve high accuracy in recognizing tennis movements.•Experimental results demonstrate superior performance over existing methods, achieving an F1 score of 0.918 with an average accuracy of 92.9 % and recognition time of 7.4 s.•This research significantly advances tennis teaching by providing timely and precise movement recognition, empowering coaches to devise targeted training plans and thereby improving overall teaching efficiency and quality. The traditional self-supervised methods based on skeleton data frequently categorize the various enhancements of a given sample as positive examples, while the remaining samples are designated as negative examples. This approach results in a significant imbalance in the ratio of positive to negative samples, which in turn constrains the efficacy of samples with identical semantic information. Therefore, to further improve the training quality in tennis teaching and enhance the accuracy of action recognition, a dual chain sharing unsupervised action recognition algorithm has been proposed. This study first designs a skeleton topology data augmentation method based on the physical connections of human joints to obtain advanced semantic embeddings. Then, an improved positive sample expansion strategy is utilized to enhance the diversity and quality of training data. Next, an unsupervised learning mechanism is employed to autonomously learn and recognize complex patterns of tennis movements. To measure the performance of the model, tests were conducted on its accuracy, F1 score, recognition time, and fitting degree. The experimental results showed that the proposed algorithm could reach its optimal state after 23 iterations of training, and its F1 value reached 0.918, with an average accuracy of 92.9 % and an average recognition time of 7.4 s. The research algorithms were superior to the multi-input branch graph convolutional network action recognition algorithms used for comparison, pull-push contrastive loss action recognition algorithms, and multi-granularity anchor contrastive representation learning action recognition algorithms. Its accuracy was leading by 8.6 %-23.5 %, and the recognition time has been reduced by 2.3s-7.4