A Smart Health Evaluation System and Optimization Mechanism for Sports Actions Incorporating BigGAN

Abstract

To meet the growing demand for sports motion recognition in training assistance, rehabilitation assessment, and intelligent monitoring, this study developed an auxiliary action-recognition evaluation framework incorporating a Big Generative Adversarial Network (BigGAN)-based data augmentation mechanism. An experimental subset was first constructed from representative sports actions in the Nanyang Technological University RGB+D120 (NTU RGB+D120) dataset, including running, jumping, throwing, and gymnastics movements. BigGAN was subsequently employed to generate augmented training samples, while truncation strategies and class-embedding mechanisms were introduced to increase pose variability and visual diversity. The generated samples were used exclusively during model training, whereas only real samples were retained in the validation and test sets to ensure an unbiased performance evaluation. The quality of the generated data was assessed using Fréchet Inception Distance (FID), Inception Score (IS), Learned Perceptual Image Patch Similarity (LPIPS), nearest-neighbor retrieval, and truncation-threshold sensitivity analysis. Samples exhibiting semantic inconsistencies, structural distortions, or near-duplicate characteristics were removed through quality control procedures. Experimental results showed that the proposed framework achieved an accuracy of 89.8%, a recall of 89.2%, and an F1-score of 89.5% on the test set. The generated samples yielded an average FID of 38.80, an average LPIPS value of 0.331, and a nearest-neighbor duplication rate of 3.5%. In addition, the BigGAN-enhanced model demonstrated more consistent classification performance across different sports categories. Running achieved the highest recognition accuracy, whereas throwing remained the most challenging category. The augmentation strategy improved the model's robustness to intra-class variability in several action categories. Nevertheless, the proposed framework provides only auxiliary support for action recognition and cannot replace professional assessments conducted by coaches, clinical practitioners, or biomechanical analysis systems. These findings offer a reproducible foundation for data augmentation, action classification, and intelligent feedback in sports motion monitoring applications.