Subject-specific body segment parameter estimation using 3D photogrammetry with multiple cameras
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. However, these quantities are not directly measurable. Current approaches include using regression models which have limited accuracy: geometr...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
PeerJ Inc.
2015-03-01
|
| Series: | PeerJ |
| Subjects: | |
| Online Access: | https://peerj.com/articles/831.pdf |
| _version_ | 1797419140313513984 |
|---|---|
| author | Kathrin E. Peyer Mark Morris William I. Sellers |
| author_facet | Kathrin E. Peyer Mark Morris William I. Sellers |
| author_sort | Kathrin E. Peyer |
| collection | DOAJ |
| description | 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. However, these quantities are 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. |
| first_indexed | 2024-03-09T06:43:39Z |
| format | Article |
| id | doaj.art-b75faf051eb64f678a5393e0e26aed3d |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:43:39Z |
| publishDate | 2015-03-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-b75faf051eb64f678a5393e0e26aed3d2023-12-03T10:42:31ZengPeerJ Inc.PeerJ2167-83592015-03-013e83110.7717/peerj.831831Subject-specific body segment parameter estimation using 3D photogrammetry with multiple camerasKathrin E. Peyer0Mark Morris1William I. Sellers2Faculty of Life Sciences, University of Manchester, Manchester, United KingdomFaculty of Life Sciences, University of Manchester, Manchester, United KingdomFaculty of Life Sciences, University of Manchester, Manchester, United KingdomInertial properties of body segments, such as mass, centre of mass or moments of inertia, are important parameters when studying movements of the human body. However, these quantities are 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.https://peerj.com/articles/831.pdfBody segment parametersPhotogrammetryStructure from motionSubject-specific estimationGeometric modellingBiomechanics |
| spellingShingle | Kathrin E. Peyer Mark Morris William I. Sellers Subject-specific body segment parameter estimation using 3D photogrammetry with multiple cameras PeerJ Body segment parameters Photogrammetry Structure from motion Subject-specific estimation Geometric modelling Biomechanics |
| title | Subject-specific body segment parameter estimation using 3D photogrammetry with multiple cameras |
| title_full | Subject-specific body segment parameter estimation using 3D photogrammetry with multiple cameras |
| title_fullStr | Subject-specific body segment parameter estimation using 3D photogrammetry with multiple cameras |
| title_full_unstemmed | Subject-specific body segment parameter estimation using 3D photogrammetry with multiple cameras |
| title_short | Subject-specific body segment parameter estimation using 3D photogrammetry with multiple cameras |
| title_sort | subject specific body segment parameter estimation using 3d photogrammetry with multiple cameras |
| topic | Body segment parameters Photogrammetry Structure from motion Subject-specific estimation Geometric modelling Biomechanics |
| url | https://peerj.com/articles/831.pdf |
| work_keys_str_mv | AT kathrinepeyer subjectspecificbodysegmentparameterestimationusing3dphotogrammetrywithmultiplecameras AT markmorris subjectspecificbodysegmentparameterestimationusing3dphotogrammetrywithmultiplecameras AT williamisellers subjectspecificbodysegmentparameterestimationusing3dphotogrammetrywithmultiplecameras |