Enhanced Basketball Shooting Performance Through Deep Pose Estimation and SAGIN‐Based Feedback Systems

ABSTRACT This paper presents a novel basketball shooting performance optimization method that integrates deep pose estimation with a Space‐Air‐Ground Integrated Network (SAGIN)‐enabled feedback mechanism. The core idea is to leverage advanced 3D human pose estimation techniques to capture the fine‐grained body kinematics during shooting, decompose these movements into interpretable motion phases, and utilize SAGIN to provide ultra‐low‐latency corrective feedback. Compared to existing methods that either focus solely on biomechanical analysis or network‐based performance enhancement, our framework establishes a closed‐loop system capable of real‐time analysis, correction, and adaptive learning. The proposed method is composed of four key components: (A) a deep pose estimation module that accurately reconstructs 3D body joints, (B) a phase‐wise motion decomposition mechanism tailored to basketball shooting, (C) a SAGIN‐based feedback pipeline that ensures low‐latency information delivery, and (D) a unified learning objective that simultaneously optimizes pose estimation accuracy and shooting biomechanics. Experimental results demonstrate that the proposed system significantly outperforms existing methods, achieving 25.4 mm MPJPE (15%–40% reduction compared to baseline methods), 90.4% shooting accuracy (12%–18% improvement over existing systems), and 38 ms feedback latency (63% reduction compared to ground‐based systems), offering a promising direction for intelligent sports training.