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Abstract
Being able to capture relevant information about elite athletes’ movement “in the wild” is challenging, especially because reference marker-based approaches hinder natural movement and are highly sensitive to environmental conditions. We propose Pose2Sim, a markerless kinematics workflow that uses OpenPose 2D pose detections from multiple views as inputs, identifies the person of interest, robustly triangulates joint coordinates from calibrated cameras, and feeds those to a 3D inverse kinematic full-body OpenSim model in order to compute biomechanically congruent joint angles. We assessed the robustness of this workflow when facing simulated challenging conditions: (Im) degrades image quality (11-pixel Gaussian blur and 0.5 gamma compression); (4c) uses few cameras (4 vs. 8); and (Cal) introduces calibration errors (1 cm vs. perfect calibration). Three physical activities were investigated: walking, running, and cycling. When averaged over all joint angles, stride-to-stride standard deviations lay between 1.7◦ and 3.2◦ for all conditions and tasks, and mean absolute errors (compared to the reference condition—Ref) ranged between 0.35◦ and 1.6◦. For walking, errors in the sagittal plane were: 1.5◦, 0.90◦, 0.19◦ for (Im), (4c), and (Cal), respectively. In conclusion, Pose2Sim provides a simple and robust markerless kinematics analysis from a network of calibrated cameras.
Original language | English |
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Article number | 6530 |
Journal | Sensors |
Volume | 21 |
Issue number | 19 |
Early online date | 30 Sept 2021 |
DOIs | |
Publication status | Published - 1 Oct 2021 |
Bibliographical note
Funding Information:Funding: This research has received funding from CNRS (Doctoral Thesis 2019), ANR Equipex PIA 2011 (project Kinovis), and ANR PPR STHP 2020 (project PerfAnalytics, ANR 20-STHP-0003).
Funding
Funding: This research has received funding from CNRS (Doctoral Thesis 2019), ANR Equipex PIA 2011 (project Kinovis), and ANR PPR STHP 2020 (project PerfAnalytics, ANR 20-STHP-0003).
Keywords
- Computer vision
- Deep learning
- Kinematics
- Markerless motion capture
- Openpose
- Opensim
- Robustness
- Sports performance analysis
ASJC Scopus subject areas
- Analytical Chemistry
- Information Systems
- Atomic and Molecular Physics, and Optics
- Biochemistry
- Instrumentation
- Electrical and Electronic Engineering
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Centre for the Analysis of Motion, Entertainment Research and Applications (CAMERA) - 2.0
Campbell, N. (PI), Cosker, D. (PI), Bilzon, J. (CoI), Campbell, N. (CoI), Cazzola, D. (CoI), Colyer, S. (CoI), Cosker, D. (CoI), Lutteroth, C. (CoI), McGuigan, P. (CoI), O'Neill, E. (CoI), Petrini, K. (CoI), Proulx, M. (CoI) & Yang, Y. (CoI)
Engineering and Physical Sciences Research Council
1/11/20 → 31/10/25
Project: Research council