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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

Subdivision of the body segments with large curvature.The first row (S) shows the high-resolution surface mesh, the second row (CH) the convex hull of the whole body segment, and the bottom row (CHD) the convex hulls of the subdivided body segments.
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fig-10: Subdivision of the body segments with large curvature.The first row (S) shows the high-resolution surface mesh, the second row (CH) the convex hull of the whole body segment, and the bottom row (CHD) the convex hulls of the subdivided body segments.

Mentions: To estimate the effect of the convex hull approximation on the mass estimation versus the original body segment shape, the volumes of a high resolution 3D body scan and of their convex hull approximation were calculated and compared. A detailed surface mesh was obtained from the National Library of Medicine’s Visible Human Project (Spitzer et al., 1996) by isosurfacing the optical slices using the VTK toolkit (http://www.vtk.org) and cleaning up the resultant mesh using Geomagic. The surface mesh of the 3D body scan was separated into body segments and the volume calculated following the same methodology as used for the point cloud data. A convex hull was applied to each body segment and the volume calculated again (see Fig. 8). The volume overestimations for each body segment (averaged between left and right) are shown Fig. 9 (column CH). Several body segments showed a large relative volume overestimation (using 10% error as a cutoff, although the choice would depend on the required accuracy): foot (26%), shank (31%), hand (47%) and forearm (16%). This is due to the relatively strong curvatures in these segments. To minimize the effect, these body segments were subdivided (see Fig. 10) and the convex hulls recalculated. The results of the divided segments are also shown in Fig. 9 (column CHD), and the decrease in volume overestimation is apparent. The volume overestimation of the subdivided foot (11%), shank (11%) and forearm (5%) are at a similar level to the other body segments and would probably be acceptable in many cases. The hands show the largest relative mass overestimation still (25%), which is due to its curved position and slightly open fingers. The convex hull error of the hand is, however, expected to be significantly smaller if the hand is imaged while being held in a straight position with no gaps between the digits.


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

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

Subdivision of the body segments with large curvature.The first row (S) shows the high-resolution surface mesh, the second row (CH) the convex hull of the whole body segment, and the bottom row (CHD) the convex hulls of the subdivided body segments.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig-10: Subdivision of the body segments with large curvature.The first row (S) shows the high-resolution surface mesh, the second row (CH) the convex hull of the whole body segment, and the bottom row (CHD) the convex hulls of the subdivided body segments.
Mentions: To estimate the effect of the convex hull approximation on the mass estimation versus the original body segment shape, the volumes of a high resolution 3D body scan and of their convex hull approximation were calculated and compared. A detailed surface mesh was obtained from the National Library of Medicine’s Visible Human Project (Spitzer et al., 1996) by isosurfacing the optical slices using the VTK toolkit (http://www.vtk.org) and cleaning up the resultant mesh using Geomagic. The surface mesh of the 3D body scan was separated into body segments and the volume calculated following the same methodology as used for the point cloud data. A convex hull was applied to each body segment and the volume calculated again (see Fig. 8). The volume overestimations for each body segment (averaged between left and right) are shown Fig. 9 (column CH). Several body segments showed a large relative volume overestimation (using 10% error as a cutoff, although the choice would depend on the required accuracy): foot (26%), shank (31%), hand (47%) and forearm (16%). This is due to the relatively strong curvatures in these segments. To minimize the effect, these body segments were subdivided (see Fig. 10) and the convex hulls recalculated. The results of the divided segments are also shown in Fig. 9 (column CHD), and the decrease in volume overestimation is apparent. The volume overestimation of the subdivided foot (11%), shank (11%) and forearm (5%) are at a similar level to the other body segments and would probably be acceptable in many cases. The hands show the largest relative mass overestimation still (25%), which is due to its curved position and slightly open fingers. The convex hull error of the hand is, however, expected to be significantly smaller if the hand is imaged while being held in a straight position with no gaps between the digits.

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