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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

Body scanner design.(A) Point cloud reconstruction with varying number of cameras. (B) Schematic representation of the RPi scanner design.
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fig-1: Body scanner design.(A) Point cloud reconstruction with varying number of cameras. (B) Schematic representation of the RPi scanner design.

Mentions: Photogrammetric reconstruction can work well with as few as 4 cameras (Sellers & Hirasaki, 2014) but more cameras are necessary to provide a relatively gap free reconstruction. To estimate the minimal number of cameras necessary to achieve a 360° reconstruction, we positioned a single camera on a circle of radius 1.6 m and placed a stationary skeletal dummy as a test subject in the centre. Images were taken every 5° and the point cloud reconstructions using 72, 36, 24, 18, 12 and 9 images, corresponding to angular resolutions ranging from 5° to 40°, were compared (see Fig. 1A). Acceptable reconstructions for the purpose of this paper, i.e., no loss of body segment features, were found with 18 or more cameras although using larger numbers of cameras certainly improved the point cloud density. After initial testing, the setup design was adjusted by increasing the radius of the camera placements (to increase the field of view to accommodate outstretched arms), placing the cameras above head-hight and angling the camera views downwards (as opposed to placing the cameras at the bottom or at hip-height) and using asymmetric patterns on the floor in the shared field of view of all cameras. The latter greatly aided the reconstruction reliability as the camera calibration algorithm relies on shared features.11


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

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

Body scanner design.(A) Point cloud reconstruction with varying number of cameras. (B) Schematic representation of the RPi scanner design.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig-1: Body scanner design.(A) Point cloud reconstruction with varying number of cameras. (B) Schematic representation of the RPi scanner design.
Mentions: Photogrammetric reconstruction can work well with as few as 4 cameras (Sellers & Hirasaki, 2014) but more cameras are necessary to provide a relatively gap free reconstruction. To estimate the minimal number of cameras necessary to achieve a 360° reconstruction, we positioned a single camera on a circle of radius 1.6 m and placed a stationary skeletal dummy as a test subject in the centre. Images were taken every 5° and the point cloud reconstructions using 72, 36, 24, 18, 12 and 9 images, corresponding to angular resolutions ranging from 5° to 40°, were compared (see Fig. 1A). Acceptable reconstructions for the purpose of this paper, i.e., no loss of body segment features, were found with 18 or more cameras although using larger numbers of cameras certainly improved the point cloud density. After initial testing, the setup design was adjusted by increasing the radius of the camera placements (to increase the field of view to accommodate outstretched arms), placing the cameras above head-hight and angling the camera views downwards (as opposed to placing the cameras at the bottom or at hip-height) and using asymmetric patterns on the floor in the shared field of view of all cameras. The latter greatly aided the reconstruction reliability as the camera calibration algorithm relies on shared features.11

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