The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
A Study of Vicon System Positioning Performance
Open accessAbstract
Motion capture setups are used in numerous fields. Studies based on motion capture data can be found in biomechanical, sport or animal science. Clinical science studies include gait analysis as well as balance, posture and motor control. Robotic applications encompass object tracking. Today’s life applications includes entertainment or augmented reality. Still, few studies investigate the positioning performance of motion capture setups. In this paper, we study the positioning performance of one player in the optoelectronic motion capture based on markers: Vicon system. Our protocol includes evaluations of static and dynamic performances. Mean error as well as positioning variabilities are studied with calibrated ground truth setups that are not based on other motion capture modalities. We introduce a new setup that enables directly estimating the absolute positioning accuracy for dynamic experiments contrary to state-of-the art works that rely on inter-marker distances. The system performs well on static experiments with a mean absolute error of 0.15 mm and a variability lower than 0.025 mm. Our dynamic experiments were carried out at speeds found in real applications. Our work suggests that the system error is less than 2 mm. We also found that marker size and Vicon sampling rate must be carefully chosen with respect to the speed encountered in the application in order to reach optimal positioning performance that can go to 0.3 mm for our dynamic study.
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Cites
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Human movement analysis using stereophotogrammetry
Leardini, A., Chiari, L., Croce, U. D., & Cappozzo, A. (2005) · Gait & Posture · doi:10.1016/j.gaitpost.2004.05.002 · Cited by 1,239
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Human movement analysis using stereophotogrammetry
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A spot check for estimating stereophotogrammetric errors
Croce, U. D., & Cappozzo, A. (2000) · Medical & Biological Engineering & Computing · doi:10.1007/bf02347045 · Cited by 61
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Cited by
On-field player workload exposure and knee injury risk monitoring via deep learning
Johnson, W. R., Mian, A., Lloyd, D. G., & Alderson, J. A. (2019) · Journal of Biomechanics · doi:10.1016/j.jbiomech.2019.07.002 · Cited by 69
Lifting Monocular Events to 3D Human Poses
Scarpellini, G., Morerio, P., & Del Bue, A. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00150 · Cited by 31
Metadata
- Authors
- Merriaux, P., Dupuis, Y., Boutteau, R., Vasseur, P., & Savatier, X.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2017-07-07
- Volume
- 17
- Issue
- 7
- Pages
- 1591
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W2724857106
- Cited by
- 435
- References
- 8
- FWCI
- 20.49
- Type
- journal article
- Language
- en
- Open access
- gold
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