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Subject-specific body segment parameter estimation using 3D photogrammetry with multiple cameras.

Peyer KE, Morris M, Sellers WI - PeerJ (2015)

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Faculty of Life Sciences, University of Manchester , Manchester , United Kingdom.

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. 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.

No MeSH data available.


Related in: MedlinePlus

Image processing work flow.Images from the RPI scanner are converted to 3D point clouds which are then scaled and segmented manually. Subsequently, convex hulling is used to produce a surface mesh around each body segment.
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fig-2: Image processing work flow.Images from the RPI scanner are converted to 3D point clouds which are then scaled and segmented manually. Subsequently, convex hulling is used to produce a surface mesh around each body segment.

Mentions: The reconstruction algorithms rely on finding matching points across multiple images so do not work well on images that contain no textural variation. We therefore experimented with using different types of clothing in the scanner, such as sports clothing, leisure clothing, and a black motion capture suit equipped with Velcro strips to aid feature detection. Clothing was either body-tight or tightened using Velcro strips if they were loose, since loose clothing would lead to an overestimation of the body volume. The participants stood in the centre of the RPi setup with their hands lifted above their head (see Fig. 2) and the 18 images were then acquired.


Subject-specific body segment parameter estimation using 3D photogrammetry with multiple cameras.

Peyer KE, Morris M, Sellers WI - PeerJ (2015)

Image processing work flow.Images from the RPI scanner are converted to 3D point clouds which are then scaled and segmented manually. Subsequently, convex hulling is used to produce a surface mesh around each body segment.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4359122&req=5

fig-2: Image processing work flow.Images from the RPI scanner are converted to 3D point clouds which are then scaled and segmented manually. Subsequently, convex hulling is used to produce a surface mesh around each body segment.
Mentions: The reconstruction algorithms rely on finding matching points across multiple images so do not work well on images that contain no textural variation. We therefore experimented with using different types of clothing in the scanner, such as sports clothing, leisure clothing, and a black motion capture suit equipped with Velcro strips to aid feature detection. Clothing was either body-tight or tightened using Velcro strips if they were loose, since loose clothing would lead to an overestimation of the body volume. The participants stood in the centre of the RPi setup with their hands lifted above their head (see Fig. 2) and the 18 images were then acquired.

Bottom Line: 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Faculty of Life Sciences, University of Manchester , Manchester , United Kingdom.

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. 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.

No MeSH data available.


Related in: MedlinePlus