APPLICATION AND EFFECT OF GENERATIVE ARTIFICIAL INTELLIGENCE IN THE INTELLIGENT EVALUATION SYSTEM FOR MULTI-DIMENSIONAL SPORTS QUALITY

To further improve the efficiency of existing sports quality evaluation methods and enhance the granularity of results, this study proposes an intelligent sports quality evaluation system based on Generative Artificial Intelligence (GAI). The system first includes a spatiotemporal motion reconstruction branch based on a Conditional Generative Adversarial Network. Meanwhile, it introduces a Gated Recurrent Unit with an attention mechanism to capture long-term temporal dependencies, thus generating high-quality motion reconstruction sequences. Second, the study uses an error detection and interpretation branch of the Variational Autoencoder (VAE), which separates style and content features through latent space analysis to accurately locate and quantify error patterns. The two branches are jointly optimized through a Dynamic Weighting Fusion (DWF) mechanism, and finally output evaluation results containing multi-dimensional quantitative indicators such as Joint Angle Error (JAE) and motion trajectory error. To fully verify the system’s performance in complex real-world scenarios, this study is based on the Physical Activity Monitoring for Age Group 2 (PAMAP2) dataset to construct a benchmark dataset named PAMAP2-Motion Enhanced (PAMAP2-ME). This dataset introduces simulated real motion noise, multi-view motion sequences, and common incorrect motion patterns demonstrated by professional coaches through GAI technology. Experimental results show that the proposed model’s motion classification accuracy is 96.2%, which is 3.7% higher than that of the baseline model, Multi-Scale-Gated 3D Convolutional Network (MS-G3D). In terms of fine-grained evaluation, the model also performs better than MS-G3D in indicators such as JAE ([Formula: see text]) and Mean Squared Error (MSE) of motion trajectory reconstruction (0.032). The above data indicate that GAI technology can improve the evaluation system’s efficiency and feedback granularity, providing reliable technical support for intelligent sports teaching and training.