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<p>To address the low recognition accuracy and high misclassification rate of existing models caused by the highly dynamic, strongly coordinated, and rhythm-bound characteristics of cheerleading movements, this study improves a Pose-based Convolutional Neural Network 3D (PoseConv3D). By embedding three core modules-dynamic skeletal correction, multi-scale spatiotemporal encoding, and motion rhythm perception integrating a dual-path classification layer, the proposed method achieves joint output of action categories and difficulty coefficients. Experiments conducted on a public benchmark dataset demonstrate that the proposed model attains an accuracy of 93.2%, representing improvements of 11.5% and 8.5% over the Spatial-Temporal Graph Convolutional Network (ST-GCN) and the original PoseConv3D, respectively. The dynamic correction module reduces joint drift error by 42.3%, while the rhythm perception module lowers the misclassification rate of “wave/kick” movements by 12.7%. Additionally, an inference speed of 88.2 frames per second meets real-time application requirements. The main contributions of this work are threefold. First, a novel dynamic-rhythm fusion recognition framework is proposed. By incorporating a rhythm-aware temporal attention mechanism and a confidence-guided skeletal correction strategy, the framework effectively addresses two key limitations of existing approaches: the insufficient modeling of temporal rhythm in graph neural networks (e.g., ST-GCN) and the poor robustness of Transformer-based methods (e.g., PoseFormer) to joint drift under high-dynamic conditions. Second, a closed-loop “recognition-difficulty quantification” system tailored to cheerleading is constructed. Finally, the proposed framework offers a transferable solution for the recognition of high-dynamic sports movements, providing methodological support for broader applications in complex action analysis.</p>

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Application of PoseConv3D algorithm in cheerleading training action recognition

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月5日

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  • Qin Li

摘要

To address the low recognition accuracy and high misclassification rate of existing models caused by the highly dynamic, strongly coordinated, and rhythm-bound characteristics of cheerleading movements, this study improves a Pose-based Convolutional Neural Network 3D (PoseConv3D). By embedding three core modules-dynamic skeletal correction, multi-scale spatiotemporal encoding, and motion rhythm perception integrating a dual-path classification layer, the proposed method achieves joint output of action categories and difficulty coefficients. Experiments conducted on a public benchmark dataset demonstrate that the proposed model attains an accuracy of 93.2%, representing improvements of 11.5% and 8.5% over the Spatial-Temporal Graph Convolutional Network (ST-GCN) and the original PoseConv3D, respectively. The dynamic correction module reduces joint drift error by 42.3%, while the rhythm perception module lowers the misclassification rate of “wave/kick” movements by 12.7%. Additionally, an inference speed of 88.2 frames per second meets real-time application requirements. The main contributions of this work are threefold. First, a novel dynamic-rhythm fusion recognition framework is proposed. By incorporating a rhythm-aware temporal attention mechanism and a confidence-guided skeletal correction strategy, the framework effectively addresses two key limitations of existing approaches: the insufficient modeling of temporal rhythm in graph neural networks (e.g., ST-GCN) and the poor robustness of Transformer-based methods (e.g., PoseFormer) to joint drift under high-dynamic conditions. Second, a closed-loop “recognition-difficulty quantification” system tailored to cheerleading is constructed. Finally, the proposed framework offers a transferable solution for the recognition of high-dynamic sports movements, providing methodological support for broader applications in complex action analysis.