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Open AccessArticle

Are Existing Monocular Computer Vision-Based 3D Motion Capture Approaches Ready for Deployment? A Methodological Study on the Example of Alpine Skiing

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Computer Vision Laboratory, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
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Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia
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Department of Computer Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
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Department of Sport Science and Kinesiology, University of Salzburg, 5400 Hallein-Rif, Austria
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Department of Orthopedics, Balgrist University Hospital, University of Zurich, 8008 Zurich, Switzerland
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(19), 4323; https://doi.org/10.3390/s19194323
Received: 2 September 2019 / Revised: 1 October 2019 / Accepted: 2 October 2019 / Published: 6 October 2019
(This article belongs to the Section Physical Sensors)
In this study, we compared a monocular computer vision (MCV)-based approach with the golden standard for collecting kinematic data on ski tracks (i.e., video-based stereophotogrammetry) and assessed its deployment readiness for answering applied research questions in the context of alpine skiing. The investigated MCV-based approach predicted the three-dimensional human pose and ski orientation based on the image data from a single camera. The data set used for training and testing the underlying deep nets originated from a field experiment with six competitive alpine skiers. The normalized mean per joint position error of the MVC-based approach was found to be 0.08 ± 0.01 m. Knee flexion showed an accuracy and precision (in parenthesis) of 0.4 ± 7.1° (7.2 ± 1.5°) for the outside leg, and −0.2 ± 5.0° (6.7 ± 1.1°) for the inside leg. For hip flexion, the corresponding values were −0.4 ± 6.1° (4.4° ± 1.5°) and −0.7 ± 4.7° (3.7 ± 1.0°), respectively. The accuracy and precision of skiing-related metrics were revealed to be 0.03 ± 0.01 m (0.01 ± 0.00 m) for relative center of mass position, −0.1 ± 3.8° (3.4 ± 0.9) for lean angle, 0.01 ± 0.03 m (0.02 ± 0.01 m) for center of mass to outside ankle distance, 0.01 ± 0.05 m (0.03 ± 0.01 m) for fore/aft position, and 0.00 ± 0.01 m2 (0.01 ± 0.00 m2) for drag area. Such magnitudes can be considered acceptable for detecting relevant differences in the context of alpine skiing. View Full-Text
Keywords: biomechanics; human pose estimation; markerless tracking; video-based 3D kinematics; technical validation; alpine ski racing biomechanics; human pose estimation; markerless tracking; video-based 3D kinematics; technical validation; alpine ski racing
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Ostrek, M.; Rhodin, H.; Fua, P.; Müller, E.; Spörri, J. Are Existing Monocular Computer Vision-Based 3D Motion Capture Approaches Ready for Deployment? A Methodological Study on the Example of Alpine Skiing. Sensors 2019, 19, 4323.

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