English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Automatic evaluating systems are fundamental issues in sports technologies.In many sports, such as figure skating, automated evaluating methods based onpose estimation have been proposed. However, previous studies have evaluatedskaters' skills in 2D analysis. In this paper, we propose an automatic edgeerror judgment system with a monocular smartphone camera and inertial sensors,which enable us to analyze 3D motions. Edge error is one of the mostsignificant scoring items and is challenging to automatically judge due to its3D motion. The results show that the model using 3D joint position coordinatesestimated from the monocular camera as the input feature had the highestaccuracy at 83% for unknown skaters' data. We also analyzed the detailed motionanalysis for edge error judgment. These results indicate that the monocularcamera can be used to judge edge errors automatically. We will provide thefigure skating single Lutz jump dataset, including pre-processed videos andlabels, at https://github.com/ryota-takedalab/JudgeAI-LutzEdge.

Automatic Edge Error Judgment in Figure Skating Using 3D Pose Estimation from a Monocular Camera and IMUs