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

Ostrek, Mirela; Rhodin, Helge; Fua, Pascal; Müller, Erich; Spörri, Jörg

doi:10.3390/s19194323

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

^1,2,

^1,3

¹,

⁴

and

Jörg Spörri

^4,5,*

Computer Vision Laboratory, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland

Faculty of Electrical Engineering and Computing, University of Zagreb, 10000 Zagreb, Croatia

Department of Computer Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada

⁴

Department of Sport Science and Kinesiology, University of Salzburg, 5400 Hallein-Rif, Austria

⁵

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)

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Abstract

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

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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. https://doi.org/10.3390/s19194323

AMA Style

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(19):4323. https://doi.org/10.3390/s19194323

Chicago/Turabian Style

Ostrek, Mirela, Helge Rhodin, Pascal Fua, Erich Müller, and Jörg Spörri. 2019. "Are Existing Monocular Computer Vision-Based 3D Motion Capture Approaches Ready for Deployment? A Methodological Study on the Example of Alpine Skiing" Sensors 19, no. 19: 4323. https://doi.org/10.3390/s19194323

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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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