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