Affiliation:
1. Faculty of Sport and Health Sciences, University of Jyväskylä, 88610 Jyväskylä, Finland
Abstract
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.
Reference36 articles.
1. Kinematic Differences between Optical Motion Capture and Biplanar Videoradiography during a Jump–Cut Maneuver;Miranda;J. Biomech.,2013
2. Kessler, S.E., Rainbow, M.J., Lichtwark, G.A., Cresswell, A.G., D’andrea, S.E., Konow, N., and Kelly, L.A. (2019). A Direct Comparison of Biplanar Videoradiography and Optical Motion Capture for Foot and Ankle Kinematics. Front. Bioeng. Biotechnol., 7.
3. Effects of Soft Tissue Artifacts on the Calculated Kinematics and Kinetics of the Knee during Stair-Ascent;Tsai;J. Biomech.,2011
4. Deep Learning-Based Human Pose Estimation: A Survey;Zheng;J. ACM,2023
5. Deep 3D Human Pose Estimation: A Review;Wang;Comput. Vis. Image Underst.,2021