Evaluation of 3D Markerless Motion Capture System Accuracy during Skate Skiing on a Treadmill
Torvinen, P., Ruotsalainen, K. S., Zhao, S., Cronin, N., Ohtonen, O., & Linnamo, V. (2024). Evaluation of 3D Markerless Motion Capture System Accuracy during Skate Skiing on a Treadmill. Bioengineering, 11(2), Article 136. https://doi.org/10.3390/bioengineering11020136
Julkaistu sarjassa
BioengineeringTekijät
Päivämäärä
2024Tekijänoikeudet
© 2024 the Authors
In this study, we developed a deep learning-based 3D markerless motion capture system for skate skiing on a treadmill and evaluated its accuracy against marker-based motion capture during G1 and G3 skating techniques. Participants performed roller skiing trials on a skiing treadmill. Trials were recorded with two synchronized video cameras (100 Hz). We then trained a custom model using DeepLabCut, and the skiing movements were analyzed using both DeepLabCut-based markerless motion capture and marker-based motion capture systems. We statistically compared joint centers and joint vector angles between the methods. The results demonstrated a high level of agreement for joint vector angles, with mean differences ranging from −2.47° to 3.69°. For joint center positions and toe placements, mean differences ranged from 24.0 to 40.8 mm. This level of accuracy suggests that our markerless approach could be useful as a skiing coaching tool. The method presents interesting opportunities for capturing and extracting value from large amounts of data without the need for markers attached to the skier and expensive cameras.
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2306-5354Asiasanat
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https://converis.jyu.fi/converis/portal/detail/Publication/202889122
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EAKR Euroopan aluekehitysrahasto, React-EULisätietoja rahoituksesta
Research was funded partly by the “Smart Track” project (ERDF, 11853/09 020101/2021 Kainuu).Lisenssi
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