Physical Education Department, Taihu University of Wuxi, Wuxi 214000, Jiangsu, China.
Comput Intell Neurosci. 2023 Feb 17;2023:2198495. doi: 10.1155/2023/2198495. eCollection 2023.
With the high-speed operation of society and the increasing development of modern science, people's quality of life continues to improve. Contemporary people are increasingly concerned about their quality of life, pay attention to body management, and strengthen physical exercise. Volleyball is a sport that is loved by many people. Studying volleyball postures and recognizing and detecting them can provide theoretical guidance and suggestions for people. Besides, when it is applied to competitions, it can also help the judges to make fair and reasonable decisions. At present, pose recognition in ball sports is challenging in action complexity and research data. Meanwhile, the research also has an important application value. Therefore, this article studies human volleyball pose recognition by combining the analysis and summary of the existing human pose recognition studies based on joint point sequences and long short-term memory (LSTM). This article proposes a data preprocessing method based on the angle and relative distance feature enhancement and a ball-motion pose recognition model based on LSTM-Attention. The experimental results show that the data preprocessing method proposed here can further improve the accuracy of gesture recognition. For example, the joint point coordinate information of the coordinate system transformation significantly improves the recognition accuracy of the five ball-motion poses by at least 0.01. In addition, it is concluded that the LSTM-attention recognition model is not only scientific in structure design but also has considerable competitiveness in gesture recognition performance.
随着社会的高速运转和现代科学的不断发展，人们的生活质量不断提高。当代人越来越关注自己的生活质量，注重身体管理，加强体育锻炼。排球是一项深受许多人喜爱的运动。研究排球姿势并识别和检测它们，可以为人们提供理论指导和建议。此外，当它应用于比赛时，也可以帮助裁判做出公平合理的决定。目前，球类运动中的姿势识别在动作复杂性和研究数据方面具有挑战性。同时，这项研究也具有重要的应用价值。因此，本文结合基于关节点序列和长短时记忆（LSTM）的现有人体姿势识别研究的分析和总结，研究了人类排球姿势识别。本文提出了一种基于角度和相对距离特征增强的数据预处理方法和一种基于 LSTM-Attention 的球运动姿势识别模型。实验结果表明，这里提出的数据预处理方法可以进一步提高手势识别的准确性。例如，坐标系变换的关节点坐标信息至少将五种球运动姿势的识别精度提高了 0.01。此外，还得出结论，LSTM-attention 识别模型不仅在结构设计上科学，而且在手势识别性能方面具有相当的竞争力。
