Department of Computer Science, Lublin University of Technology, 20-618 Lublin, Poland.
Sensors (Basel). 2020 Oct 27;20(21):6094. doi: 10.3390/s20216094.
Human movement analysis is very often applied to sport, which has seen great achievements in assessing an athlete's progress, giving further training tips and in movement recognition. In tennis, there are two basic shots: forehand and backhand, which are performed during all matches and training sessions. Recognition of these movements is important in the quantitative analysis of a tennis game. In this paper, the authors propose using Spatial-Temporal Graph Neural Networks (ST-GCN) to challenge the above task. Recognition of the shots is performed on the basis of images obtained from 3D tennis movements (forehands and backhands) recorded by the Vicon motion capture system (Oxford Metrics Ltd, Oxford, UK), where both the player and the racket were recorded. Two methods of putting data into the ST-GCN network were compared: with and without fuzzying of data. The obtained results confirm that the use of fuzzy input graphs for ST-GCNs is a better tool for recognition of forehand and backhand tennis shots relative to graphs without fuzzy input.
人体运动分析在体育运动中得到了广泛应用，在评估运动员的进步、提供进一步的训练建议和运动识别方面取得了巨大成就。在网球中，有两种基本的击球方式：正手和反手，这两种击球方式在所有比赛和训练中都有使用。识别这些动作对于网球比赛的定量分析很重要。在本文中，作者提出使用时空图卷积神经网络（ST-GCN）来挑战上述任务。通过使用 Vicon 运动捕捉系统（牛津度量有限公司，英国牛津）记录的 3D 网球运动（正手和反手）获得的图像来进行击球的识别，该系统记录了球员和球拍。比较了两种将数据输入 ST-GCN 网络的方法：有和没有数据模糊化。得到的结果证实，相对于没有模糊输入的图，使用模糊输入图的 ST-GCN 是识别网球正手和反手击球的更好工具。
