Cross-validation of water clarity.

Human swimming posture recognition is a key technology to improve training effect and reduce sports injury by analyzing and recognizing swimmer’s movement posture. However, the existing technical means cannot accomplish the accurate recognition of human swimming posture in underwater environment with high standard. For this reason, the study takes the 3D convolutional neural network as the model basis, and introduces the global average pooling and batch normalization to optimize its network structure and data processing, respectively. Meanwhile, full pre-activation residual network and three-branch structure convolutional attention mechanism are added to improve the feature extraction and recognition. Finally, a novel human swimming posture recognition model is proposed. The outcomes revealed that this model had the highest recognition accuracy of 95%, the highest recall of 93.26% and the highest F1 value of 92.87%. The lowest pose recognition errors were up to 4.7%, 4.9%, 2.1% and 6.6% for freestyle, breaststroke, butterfly and backstroke, respectively. The shortest recognition time was 6.78 s for the freestyle item, which minimized the recognition time and reduced the recognition error compared with the same type of recognition model. The new model proposed by the research shows significant advantages in recognition accuracy and computational efficiency. It can provide more effective support for recognizing athletes’ swimming posture for future swimming endeavors.

人体游泳姿态识别是通过分析与识别人体游泳动作姿态，进而提升训练效果、降低运动损伤的核心技术。然而现有技术手段难以在严苛的水下环境中实现高精度的人体游泳姿态识别。为此，本研究以3D卷积神经网络（3D Convolutional Neural Network）作为模型基底，分别引入全局平均池化（global average pooling）与批量归一化（batch normalization），对其网络结构与数据处理流程进行优化。同时，加入全预激活残差网络（full pre-activation residual network）与三分支结构卷积注意力机制（three-branch structure convolutional attention mechanism），以强化特征提取与识别能力。最终提出一种新型人体游泳姿态识别模型。实验结果表明，该模型的最高识别准确率达95%，最高召回率为93.26%，最高F1值为92.87%。针对自由泳、蛙泳、蝶泳与仰泳，其最低姿态识别误差分别仅为4.7%、4.9%、2.1%与6.6%。其中自由泳项目的最短识别耗时为6.78秒，相较同类识别模型，该模型有效缩短了识别时长并降低了识别误差。本研究提出的新型模型在识别精度与计算效率方面均展现出显著优势，可为未来游泳运动中运动员游泳姿态的识别提供更为有效的技术支撑。