Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 1: Robustness
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 O...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/19/6530 |
| _version_ | 1797515709392093184 |
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| author | David Pagnon Mathieu Domalain Lionel Reveret |
| author_facet | David Pagnon Mathieu Domalain Lionel Reveret |
| author_sort | David Pagnon |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T06:51:07Z |
| format | Article |
| id | doaj.art-61f38ae07bcf4109a045151decdf7872 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T06:51:07Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-61f38ae07bcf4109a045151decdf78722023-11-22T16:47:41ZengMDPI AGSensors1424-82202021-09-012119653010.3390/s21196530Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 1: RobustnessDavid Pagnon0Mathieu Domalain1Lionel Reveret2Laboratoire Jean Kuntzmann, Université Grenoble Alpes, UMR CNRS 5224, 38330 Montbonnot-Saint-Martin, FranceInstitut Pprime, Université de Poitiers, CNRS UPR 3346, 86360 Chasseneuil-du-Poitou, FranceLaboratoire Jean Kuntzmann, Université Grenoble Alpes, UMR CNRS 5224, 38330 Montbonnot-Saint-Martin, FranceBeing 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.https://www.mdpi.com/1424-8220/21/19/6530markerless motion capturesports performance analysiskinematicscomputer visionopenposeopensim |
| spellingShingle | David Pagnon Mathieu Domalain Lionel Reveret Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 1: Robustness Sensors markerless motion capture sports performance analysis kinematics computer vision openpose opensim |
| title | Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 1: Robustness |
| title_full | Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 1: Robustness |
| title_fullStr | Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 1: Robustness |
| title_full_unstemmed | Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 1: Robustness |
| title_short | Pose2Sim: An End-to-End Workflow for 3D Markerless Sports Kinematics—Part 1: Robustness |
| title_sort | pose2sim an end to end workflow for 3d markerless sports kinematics part 1 robustness |
| topic | markerless motion capture sports performance analysis kinematics computer vision openpose opensim |
| url | https://www.mdpi.com/1424-8220/21/19/6530 |
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