With the advancement of artificial intelligence, 3D human pose estimation-based systems for sports training and posture correction have gained significant attention in adolescent sports. However, existing methods face challenges in handling complex movements, providing real-time feedback, and accommodating diverse postures, particularly with occlusions, rapid movements, and the resource constraints of Internet of Things (IoT) devices, making it difficult to balance accuracy and real-time performance. To address these issues, we propose GTA-Net, an intelligent system for posture correction and real-time feedback in adolescent sports, integrated within an IoT-enabled environment. This model enhances pose estimation in dynamic scenes by incorporating Graph Convolutional Networks (GCN), Temporal Convolutional Networks (TCN), and Hierarchical Attention mechanisms, achieving real-time correction through IoT devices. Experimental results show GTA-Net's superior performance on Human3.6M, HumanEva-I, and MPI-INF-3DHP datasets, with Mean Per Joint Position Error (MPJPE) values of 32.2mm, 15.0mm, and 48.0mm, respectively, significantly outperforming existing methods. The model also demonstrates strong robustness in complex scenarios, maintaining high accuracy even with occlusions and rapid movements. This system enhances real-time posture correction and offers broad applications in intelligent sports and health management.

翻译：随着人工智能的发展，基于三维人体姿态估计的运动训练与姿态矫正系统在青少年体育领域受到广泛关注。然而，现有方法在处理复杂动作、提供实时反馈以及适应多样化姿态方面面临挑战，尤其是在存在遮挡、快速运动以及物联网设备资源受限的情况下，难以平衡精度与实时性。为解决这些问题，我们提出GTA-Net，一种在物联网支持环境下集成的、用于青少年运动姿态矫正与实时反馈的智能系统。该模型通过融合图卷积网络、时序卷积网络和分层注意力机制，增强了动态场景下的姿态估计能力，并借助物联网设备实现实时矫正。实验结果表明，GTA-Net在Human3.6M、HumanEva-I和MPI-INF-3DHP数据集上均取得优异性能，其平均每关节位置误差分别为32.2毫米、15.0毫米和48.0毫米，显著优于现有方法。该模型在复杂场景下亦表现出强鲁棒性，即使在遮挡和快速运动情况下仍能保持高精度。本系统提升了实时姿态矫正效果，在智能体育与健康管理领域具有广阔的应用前景。