Automatic evaluating systems are fundamental issues in sports technologies. In many sports, such as figure skating, automated evaluating methods based on pose estimation have been proposed. However, previous studies have evaluated skaters' skills in 2D analysis. In this paper, we propose an automatic edge error judgment system with a monocular smartphone camera and inertial sensors, which enable us to analyze 3D motions. Edge error is one of the most significant scoring items and is challenging to automatically judge due to its 3D motion. The results show that the model using 3D joint position coordinates estimated from the monocular camera as the input feature had the highest accuracy at 83% for unknown skaters' data. We also analyzed the detailed motion analysis for edge error judgment. These results indicate that the monocular camera can be used to judge edge errors automatically. We will provide the figure skating single Lutz jump dataset, including pre-processed videos and labels, at https://github.com/ryota-takedalab/JudgeAI-LutzEdge.

翻译：自动评估系统是体育技术中的基础性问题。在花样滑冰等运动中，已提出了基于姿态估计的自动化评估方法。然而，此前研究多限于二维层面对运动员技能进行评估。本文提出了一种基于单目智能手机摄像头与惯性传感器的用刃错误自动判别系统，可分析三维运动。用刃错误是评分中最重要的项目之一，因其涉及三维运动而难以自动判别。实验结果表明，以单目摄像头估计的三维关节位置坐标为输入特征时，对未知运动员数据的判别准确率最高达83%。我们还对用刃错误判别的精细运动分析进行了研究。这些结果表明，单目摄像头可用于自动判别用刃错误。我们将提供包含预处理视频及标签的花样滑冰单跳鲁兹跳数据集，访问地址为https://github.com/ryota-takedalab/JudgeAI-LutzEdge。