School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, JS,China.
Nanjing Sport Institute, Nanjing, JS,China.
Pediatr Exerc Sci. 2024 Sep 30;36(4):274-288. doi: 10.1123/pes.2024-0025. Print 2024 Nov 1.
The popularity of fencing and intense sports competition has burdened adolescents with excessive training, harming their immature bodies. Traditional training methods fail to provide timely movement corrections and personalized plans, leading to ineffective exercises. This paper aims to use artificial intelligence technology to reduce ineffective exercises and alleviate the training burden.
We propose an action recognition algorithm based on the characteristics of adolescent athletes. This algorithm uses multimodal input data to comprehensively extract action information. Each modality is processed by the same network structure, utilizing attention mechanisms and adaptive graph structures. A multibranch feature fusion method is used to determine the final action category.
We gathered the fencing footwork data set 2.0. Our model achieved 93.3% accuracy, with the highest precision at 95.8% and the highest F1-Score at 94.5% across all categories. It effectively recognized actions of adolescents with different heights and speeds, outperforming traditional methods.
Our artificial intelligence-based training solution improves training efficiency and reduces the training burden on adolescents.
击剑等剧烈运动竞赛的普及，使青少年面临过度训练的负担，对其尚未成熟的身体造成伤害。传统的训练方法无法提供及时的动作纠正和个性化计划，导致训练效果不佳。本研究旨在利用人工智能技术减少无效训练，减轻训练负担。
我们提出了一种基于青少年运动员特点的动作识别算法。该算法使用多模态输入数据全面提取动作信息。每个模态都通过相同的网络结构进行处理，利用注意力机制和自适应图结构。采用多分支特征融合方法确定最终的动作类别。
我们收集了击剑步法数据集 2.0。我们的模型准确率达到 93.3%，在所有类别中，最高精度达到 95.8%，最高 F1-Score 达到 94.5%。它可以有效识别不同身高和速度的青少年的动作，优于传统方法。
我们基于人工智能的训练解决方案提高了训练效率，减轻了青少年的训练负担。
