3D Visualization of Body Motion in Speed Climbing
Speed climbing involves an optimization of the velocity of the ascent and the trajectory path during performance. Consequently, any amount of energy spent in the two other directions than vertical, namely the lateral direction and the direction perpendicular to the wall plane, is a potential loss of...
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2020-09-01
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doaj-340c5d7520d84be6b2058f5c4e8743b02020-11-25T03:54:38ZengFrontiers Media S.A.Frontiers in Psychology1664-10782020-09-011110.3389/fpsyg.2020.021885318973D Visualization of Body Motion in Speed ClimbingLionel Reveret0Sylvain Chapelle1Franck Quaine2Pierre Legreneur3LJK, University Grenoble Alpes, CNRS, INRIA, Montbonnot-Saint-Martin, FranceFFME, Fédération Française d'Escalade et de Montagne, Paris, FranceGIPSA, University Grenoble Alpes, CNRS, Saint Martin d'Hères, FranceLIBM, University Lyon 1, Lyon, FranceSpeed climbing involves an optimization of the velocity of the ascent and the trajectory path during performance. Consequently, any amount of energy spent in the two other directions than vertical, namely the lateral direction and the direction perpendicular to the wall plane, is a potential loss of performance. To assess this principle, we present a study on 3D motion analysis and its 3D visualization for a subject during a speed climbing performance. The fundamentals of geometrical measurement in 3D require to integrate multiple 2D cues, at least two, in order to extract 3D information. First results with two drones following an athlete's ascent show that a 3D velocity profile can be provided from the tracking of a marker on the harness, pointing critical phases in the ascent where the vertical speed is not dominant any more. We further investigate 3D motion of full body using markerless video-based tracking. Our approach is based on a full body 3D avatar model of the climber, represented as a 3D mesh. This model and its deformation are learned in a laboratory studio. The learning needs to be done only once. Result is a manifold embedding of the 3D mesh and its deformations, which can be used afterwards to perform registration onto video of performance of speed climbing. The results of the tracking is an inference of the 3D mesh aligned onto videos of speed climbing performance. From this 3D mesh, we deduce an estimation of the center of mass (COM). We show that this estimation from 3D mesh differs from the usual approximation of the COM as a marker on the harness. In particular, the 3D mesh COM takes into account the whole body movement such as the influence of the limbs which is not detected by a marker on the harness.https://www.frontiersin.org/article/10.3389/fpsyg.2020.02188/fullspeed climbingvideo analysisbiomechanicsmotion analysis3D visualizationcenter of mass |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Lionel Reveret Sylvain Chapelle Franck Quaine Pierre Legreneur |
spellingShingle |
Lionel Reveret Sylvain Chapelle Franck Quaine Pierre Legreneur 3D Visualization of Body Motion in Speed Climbing Frontiers in Psychology speed climbing video analysis biomechanics motion analysis 3D visualization center of mass |
author_facet |
Lionel Reveret Sylvain Chapelle Franck Quaine Pierre Legreneur |
author_sort |
Lionel Reveret |
title |
3D Visualization of Body Motion in Speed Climbing |
title_short |
3D Visualization of Body Motion in Speed Climbing |
title_full |
3D Visualization of Body Motion in Speed Climbing |
title_fullStr |
3D Visualization of Body Motion in Speed Climbing |
title_full_unstemmed |
3D Visualization of Body Motion in Speed Climbing |
title_sort |
3d visualization of body motion in speed climbing |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Psychology |
issn |
1664-1078 |
publishDate |
2020-09-01 |
description |
Speed climbing involves an optimization of the velocity of the ascent and the trajectory path during performance. Consequently, any amount of energy spent in the two other directions than vertical, namely the lateral direction and the direction perpendicular to the wall plane, is a potential loss of performance. To assess this principle, we present a study on 3D motion analysis and its 3D visualization for a subject during a speed climbing performance. The fundamentals of geometrical measurement in 3D require to integrate multiple 2D cues, at least two, in order to extract 3D information. First results with two drones following an athlete's ascent show that a 3D velocity profile can be provided from the tracking of a marker on the harness, pointing critical phases in the ascent where the vertical speed is not dominant any more. We further investigate 3D motion of full body using markerless video-based tracking. Our approach is based on a full body 3D avatar model of the climber, represented as a 3D mesh. This model and its deformation are learned in a laboratory studio. The learning needs to be done only once. Result is a manifold embedding of the 3D mesh and its deformations, which can be used afterwards to perform registration onto video of performance of speed climbing. The results of the tracking is an inference of the 3D mesh aligned onto videos of speed climbing performance. From this 3D mesh, we deduce an estimation of the center of mass (COM). We show that this estimation from 3D mesh differs from the usual approximation of the COM as a marker on the harness. In particular, the 3D mesh COM takes into account the whole body movement such as the influence of the limbs which is not detected by a marker on the harness. |
topic |
speed climbing video analysis biomechanics motion analysis 3D visualization center of mass |
url |
https://www.frontiersin.org/article/10.3389/fpsyg.2020.02188/full |
work_keys_str_mv |
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