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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

Male visible human segment mass (as % of body mass) of the high-resolution mesh, convex hull, regression model and average values.S, High-resolution surface mesh; CH, Convex Hull of whole body segments; CHD, Convex Hull with subdivided body segments (only segments indicated with an * were subdivided as shown in Fig. 10); ZR, Values predicted using Zatsiosrky’s linear regression model (using weight and height); Z, Male average values reported by Zatsiorsky; D, Male average values reported by Dempster (Dempster, 1955; Leva, 1996; Zatsiorsky, 2002).
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fig-11: Male visible human segment mass (as % of body mass) of the high-resolution mesh, convex hull, regression model and average values.S, High-resolution surface mesh; CH, Convex Hull of whole body segments; CHD, Convex Hull with subdivided body segments (only segments indicated with an * were subdivided as shown in Fig. 10); ZR, Values predicted using Zatsiosrky’s linear regression model (using weight and height); Z, Male average values reported by Zatsiorsky; D, Male average values reported by Dempster (Dempster, 1955; Leva, 1996; Zatsiorsky, 2002).

Mentions: Figure 11 contains the relative mass estimations of the original surface mesh, the convex hulls with and without subdivision, and the average and regression model values found in literature. With a BMI value of almost 28, the male visible human is not well represented by the average or regression model values found in literature, where the majority of the studies involve relatively athletic people (BMI average of around 24) or obese individuals (BMI over 30). The convex hulls of the subdivided segments (CHD in Fig. 11) give the closest approximation to the original mesh and, with the exception of the hands, are within a relative error of less than 5%. The relative error of the convex hull of the whole segments (CH in Fig. 11) is larger but closer to the original mesh than average and regression values given in literature. The moments of inertia are overestimated as well as they are a product of the mass of the segment. Their overestimation follows the same trend as the mass overestimation, i.e., the largest overestimation occurs for the hands, followed by the shanks and feet (see Fig. S2 in Supplemental Information), and the subdivided segments produce more accurate values with an average relative error below 10%.


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

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

Male visible human segment mass (as % of body mass) of the high-resolution mesh, convex hull, regression model and average values.S, High-resolution surface mesh; CH, Convex Hull of whole body segments; CHD, Convex Hull with subdivided body segments (only segments indicated with an * were subdivided as shown in Fig. 10); ZR, Values predicted using Zatsiosrky’s linear regression model (using weight and height); Z, Male average values reported by Zatsiorsky; D, Male average values reported by Dempster (Dempster, 1955; Leva, 1996; Zatsiorsky, 2002).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig-11: Male visible human segment mass (as % of body mass) of the high-resolution mesh, convex hull, regression model and average values.S, High-resolution surface mesh; CH, Convex Hull of whole body segments; CHD, Convex Hull with subdivided body segments (only segments indicated with an * were subdivided as shown in Fig. 10); ZR, Values predicted using Zatsiosrky’s linear regression model (using weight and height); Z, Male average values reported by Zatsiorsky; D, Male average values reported by Dempster (Dempster, 1955; Leva, 1996; Zatsiorsky, 2002).
Mentions: Figure 11 contains the relative mass estimations of the original surface mesh, the convex hulls with and without subdivision, and the average and regression model values found in literature. With a BMI value of almost 28, the male visible human is not well represented by the average or regression model values found in literature, where the majority of the studies involve relatively athletic people (BMI average of around 24) or obese individuals (BMI over 30). The convex hulls of the subdivided segments (CHD in Fig. 11) give the closest approximation to the original mesh and, with the exception of the hands, are within a relative error of less than 5%. The relative error of the convex hull of the whole segments (CH in Fig. 11) is larger but closer to the original mesh than average and regression values given in literature. The moments of inertia are overestimated as well as they are a product of the mass of the segment. Their overestimation follows the same trend as the mass overestimation, i.e., the largest overestimation occurs for the hands, followed by the shanks and feet (see Fig. S2 in Supplemental Information), and the subdivided segments produce more accurate values with an average relative error below 10%.

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