THE CONSTRUCTION OF OUTDOOR PHYSICAL TRAINING ASSESSMENT MODEL UNDER ARTIFICIAL INTELLIGENCE AND NEURAL NETWORKS
ZHIHUI LI, Shamsulariffin Samsudin, YUXIN CONG, Kaihao Yu · Journal of Mechanics in Medicine and Biology · 2026
To meet the demand for efficient skeletal keypoint recognition in outdoor physical training assessment, this study introduces a lightweight Cross Stage Partial Poseur Network (CSP-Poseur). The proposed model enhances the original Poseur backbone through an improved Cross Stage Partial (CSP) structure that minimizes parameter redundancy. It further integrates a Convolutional Block Attention Module (CBAM) and a Gated Attention Unit (GAU) to strengthen feature discrimination in key joint regions and improve adaptability to complex environments. Experiments show that CSP-Poseur achieves superior performance across multiple datasets. On the COCO dataset, it attains a mean Average Precision (mAP) of 76.7%, with AP50 of 91.3% and AP75 of 83.9%, outperforming the baseline Poseur by 1.72%, 0.88% and 2.07%, respectively. On the MPII dataset, it reaches an mAP of 90.9%, exceeding Poseur by 0.44%. Despite these gains, the model remains highly efficient, requiring only 14.9 M parameters and 1.18 G FLOPs, both considerably lower than mainstream approaches. Ablation studies verify that CBAM and GAU significantly enhance skeletal keypoint modeling, while experiments on the decoding structure reveal that a four-layer decoder offers the best balance between accuracy and computational cost. Overall, CSP-Poseur achieves an effective trade-off between precision and efficiency, making it well-suited for real-time pose estimation and training action evaluation on edge devices.