Subject-specific body segment parameter estimation using 3D photogrammetry with multiple cameras
- Published
- Accepted
- Subject Areas
- Anthropology, Computational Biology, Mathematical Biology, Computational Science
- Keywords
- body segment parameters, photogrammetry, structure from motion, subject-specific estimation, geometric modelling, biomechanics
- Copyright
- © 2014 Peyer et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ PrePrints) and either DOI or URL of the article must be cited.
- Cite this article
- 2014. Subject-specific body segment parameter estimation using 3D photogrammetry with multiple cameras. PeerJ PrePrints 2:e573v1 https://doi.org/10.7287/peerj.preprints.573v1
Abstract
Inertial properties of body segments, such as mass, centre of mass or moments of inertia, are important parameters when studying movements of the human body. These quantities are, however, not directly measurable. Current approaches include using regression models which have limited accuracy; geometric models with lengthy measuring procedures; or acquiring and post-processing MRI scans of participants. We propose a geometric methodology based on 3D photogrammetry using multiple cameras to provide subject-specific body segment parameters while minimizing the interaction time with the participants. A low-cost body scanner was built using multiple cameras and 3D point cloud data generated using structure from motion photogrammetric reconstruction algorithms. The point cloud was manually separated into body segments and convex hulling applied to each segment to produce the required geometric outlines. The accuracy of the method can be adjusted by choosing the number of subdivisions of the body segments. The body segment parameters of six participants (four male and two female) are presented using the proposed method. The multi-camera photogrammetric approach is expected to be particularly suited for studies including populations for which regression models are not available in literature and where other geometric techniques or MRI scanning are not applicable due to time or ethical constraints.
Author Comment
We propose a geometric methodology based on 3D photogrammetry using multiple cameras to provide subject-specific body segment parameters while minimizing the interaction time with the participants. A low-cost body scanner was built using multiple cameras and 3D point cloud data generated using structure from motion photogrammetric reconstruction algorithms. This work will be submitted to PeerJ for review.
Supplemental Information
Appendices and supplementary figures and tables
Supplementary Information