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.

Inertial body segment parameters (BSP) such as mass, centre of mass (CoM) or moment of inertia are used in motion analysis in research as well as in clinical settings. Accurate values are essential for techniques such as inverse dynamic analysis to allow the calculation of joint torques based on measured segmental accelerations (

Recently, other methods have been explored to obtain volumetric data of body segments that, in combination with body density assumptions, can provide subject-specific inertial BSP.

The aim of this paper is to investigate whether an approach based on structure form motion photogrammetric reconstruction can provide person-specific body segment parameters, and to identify the strength and weaknesses of such an approach with regards to ease of implementation, cost-effectiveness, subject comfort and processing time. A low-cost body scanner was built using multiple cameras and the body segment parameters of six participants (four male and two female) are presented using the proposed method.

Photogrammetry relies on obtaining multiple photographs taken from different locations. These photographs can be taken with any suitable device, and for objects that do not move, the most cost-effective option is to take 50 + photographs with a single camera that is moved around the object. This has the additional advantage that a single intrinsic calibration can be used, since the camera optics can be considered identical for multiple images. However, for subjects that can move, all the photographs must be taken simultaneously so that the subject is in exactly the same position for all the images. Simultaneous photographs can be achieved in several different ways including multiple still cameras with synchronised remote controls, multiple USB web cameras, or multiple networked cameras. There is probably little to choose between these methods, but initial experimentation found that network/IP cameras provided a cost-effective solution that scaled well. The camera resolution should be as high as reasonably possible, since higher resolution images provide more information for the feature extraction algorithms and higher point density in the eventual reconstruction. This means that low-resolution cameras such as low cost web cameras and standard resolution video cameras may not be suitable.

Most applications that employ photogrammetry aim to capture surface data in great detail, with the emphasis on creating almost true-to-live 3D models and thus maximizing the point cloud density. Some applications require only the information available from the point cloud directly (such as feature point locations) and do not require a surface mesh. In fact, meshing algorithms tend to decrease the accuracy of the model (

Photogrammetric reconstruction can work well with as few as 4 cameras (^{1}

Without the patterns on the floor, the camera calibration relied on shared features found on the subject, whereas the patterned floor provided a large (or even completely sufficient) number of features to run the camera calibration algorithm.

The network camera was implemented using Raspberry Pi (RPi) modules, type A, each equipped with an 8GB SD card and a Pi camera ((A) Point cloud reconstruction with varying number of cameras. (B) Schematic representation of the RPi scanner design.

RPi cameras can record either still images or movie files. For this application we needed to trigger all the cameras to record a single image at the same instant. This was achieved using the open source “Compound Pi” application (

Full body scans using the RPi setup were obtained from six voluntary participants. Additionally, their body weight and height was measured (

P1 (m) | P2 (m) | P3 (m) | P4 (m) | P5 (f) | P6 (f) | VH (m) | |
---|---|---|---|---|---|---|---|

Mass (kg) | 73.4 | 77.0 | 88.2 | 87.8 | 65.4 | 55.2 | 90.3 |

Height (m) | 1.81 | 1.83 | 1.85 | 1.83 | 1.65 | 1.58 | 1.80 |

Participants (m: male, f: female)

Male visible human

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

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.

The 3D point cloud reconstruction was initially done using open source application VisualSFM (^{2}

This is based on the comparison of the best reconstruction result achieved with each software after testing an extensive, but not complete, combination of reconstruction parameters.

The parameters used in the reconstructions are reported in^{®}(

^{®}(see

^{3}was chosen, while a uniform density of 1000 kg/m

^{3}was assumed for all other body segments (

Six participants were scanned using the RPi photogrammetry setup and their point cloud segmented. In order to be able to calculate the inertial properties, the point cloud needs to be converted into a closed surface mesh. To calculate the volume of an arbitrary shape defined by a surface mesh, the mesh needs to be well defined, i.e., it should be two-manifold, contain no holes in the mesh, and have coherent face orientations. The process of converting a point cloud to a well-defined mesh is known as hulling and there are several possible methods available. The simplest is the minimum convex hull where the minimum volume convex shape is derived mathematically from the point cloud (

P, Average value of all six participants (error bars show standard deviation). Foot mass adjusted by a factor of 0.51 to compensate for volume overestimation due to wearing shoes. Z(m), Male average values reported by Zatsiorsky; Z(f), Female average values reported by Zatsiorsky (

P, Average value of all six participants (error bars show standard deviation). Foot moment of inertia adjusted by a factor of 0.51 to compensate for volume overestimation due to wearing shoes. Z(m), Male average values reported by Zatsiorsky; Z(f), Female average values reported by Zatsiorsky (

The absolute values of Ixy, Ixz and Iyz are shown together with a positive error bar (negative error bar is symmetrical) equal to one standard deviation. The signed values are reported in ^{3} kg m^{2} and is not displayed. Foot products of inertia adjusted by a factor of 0.51 to compensate for volume overestimation due to wearing shoes.

P, Average value of all six participants (error bars show standard deviation). Z(m, male; f, female): Average values by Zatsiorsky, adjusted by de Leva. The CoM is given as % of the segment length. The definition of the segments and reference points are given in

Average values of all six participants are shown (error bars show standard deviation). Due to mirror-symmetry, the y-values of the segments on the left- and right-hand side have opposite signs. To calculate the average, the sign of the segments on the left-hand side was inverted. The CoM is given as % of the segment length. The data of the foot is not included due to the participants wearing shoes.

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 (

(A) High-resolution surface mesh. (B) Convex hull mesh.

Data shown as the relative difference of the hull with respect to the original mesh. CH, Convex hull of body segment; CHD, Convex hull of divided body segments (only segments indicated with an * were subdivided, see

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.

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

We can see from the results that the proposed methodology produces values that are very similar to those derived using regression equations. There are no consistent problems, although it is clearly important that the hand is held in a suitable flat position but with fingers adducted so that the hulling can provide an accurate volume estimation. We would expect that the photogrammetric process will work as well as any of the published geometrical approaches (

In general, regression equations work well for applicable populations and are probably more suitable if body mass distribution is not a major focal point of the research, particularly given that in some cases it can be shown that experimental outcomes are not especially sensitive to the BSP parameters chosen (

However there are some specific issues with this technique that could to be improved for a more streamlined and potentially more accurate workflow (see

(A) Photogrammetry; (B) Body segmentation; (C) Segment hulling; (D) Inertial parameter estimation.

Convex hulling of the point cloud is a robust and fast way to produce surface meshes. The fact that it systematically overestimates the volume of concave features can be improved by subdividing body segments into smaller parts and the decision then becomes what level of subdivision is appropriate for an acceptable level of accuracy (see

The adoption of one of the concave hulling techniques is likely to lead to a similar level of improvement again with a minimum (but not zero) level of additional work. The level of subdivision required not only depends on the body segment, but also the population studied so it may well be appropriate that the segmentation level is adjusted according to the type of study and its sensitivity to inaccuracies in the BSP (i.e., multiple segment subdivisions increase accuracy of volume estimation). In this work, a uniform scaling factor and constant body density (apart from the trunk) was assumed. It is well known that the density varies among body segments as well as among populations due to different percentages of fat and muscle tissue (

In terms of technology, the current arrangement of using 18 Raspberry Pi cameras is reasonably straightforward and relatively inexpensive. It requires no calibration before use, and the process of moving the subject into the target area is extremely quick. However, it does take up a great deal of room in the laboratory, and the current software is reliant on clothing contrast for the reconstructions, which limits the flexibility of the technique. This could be improved by projecting a structured light pattern onto the subject so that areas with minimal contrast can be reconstructed accurately (

One future use of this technology is clearly the use of such systems and algorithms for complete motion capture (

A methodology based on structure form motion photogrammetric reconstruction has been presented that provides subject-specific body segment parameters. The method relies on the surface depth information extracted from multiple photographs of a participant, taken simultaneously from multiple different view points. The brief interaction time with the participants (taking all required photos simultaneously, and measuring the height and weight only) makes this a promising method in studies with vulnerable subjects or where cost or ethical constraints do not allow the use of other imaging methods such as CT or MRI scans. Unlike regression models that are valid only for a small population sample, we expect the proposed methodology to be able to perform equally well for a wide range of population samples. The post-processing time is lengthy compared to using regression models or average values from literature but not compared to processing MRI or CT data. The 3D scanner presented in this paper was able to produce a sufficient 3D data points to estimate body segment volumes with only 18 RPi cameras, which kept the hardware cost to a minimum. Depending on the accuracy required for the project, we would expect both more cameras and higher resolution cameras to improve the robustness of the 3D point cloud reconstruction.

While the results presented in this work were derived using commercial software such as AgiSoft, Geomagic and Matlab^{®}, we were able to to achieve similar results using open-source software only (such as VisualFMS (

The authors would like to thank Dave Jones for the development of the Compound Pi programme and his generous help with the network setup of the Raspberry Pi scanner.

The authors declare there are no competing interests.

The following information was supplied relating to ethical approvals (i.e., approving body and any reference numbers):

The experimental protocol (reference number 13310) was approved by the University of Manchester ethics panel.