Estimating body segment parameters from three-dimensional human body scans

Body segment parameters are inputs for a range of applications. Participant-specific estimates of body segment parameters are desirable as this requires fewer prior assumptions and can reduce outcome measurement errors. Commonly used methods for estimating participant-specific body segment parameters are either expensive and out of reach (medical imaging), have many underlying assumptions (geometrical modelling) or are based on a specific subset of a population (regression models). Our objective was to develop a participant-specific 3D scanning and body segmentation method that estimates body segment parameters without any assumptions about the geometry of the body, ethnic background, and gender, is low-cost, fast, and can be readily available. Using a Microsoft Kinect Version 2 camera, we developed a 3D surface scanning protocol that enabled the estimation of participant-specific body segment parameters. To evaluate our system, we performed repeated 3D scans of 21 healthy participants (10 male, 11 female). We used open source tools to segment each body scan into 16 segments (head, torso, abdomen, pelvis, left and right hand, forearm, upper arm, foot, shank and thigh) and wrote custom software to estimate each segment’s mass, mass moment of inertia in the three principal orthogonal axes relevant to the center of the segment, longitudinal length, and center of mass. We compared our body segment parameter estimates to those obtained using two comparison methods and found that our system was consistent in estimating total body volume between repeated scans (male p = 0.1194, female p = 0.2240), estimated total body mass without significant differences when compared to our comparison method and a medical scale (male p = 0.8529, female p = 0.6339), and generated consistent and comparable estimates across a range of the body segment parameters of interest. Our work here outlines and provides the code for an inexpensive 3D surface scanning method for estimating a range of participant-specific body segment parameters.

Layer by Layer: The Manufacture of Graeco-Roman Funerary Masks

Ptolemaic cartonnage masks were produced by layering textiles – or reused papyrus sheets – with plaster and glue. Despite the use of the same basic components, the process of manufacture could vary depending on shape, size, time and place. This article aims to clarify the production methods and the different phases of manufacture of these masks, using different imagery techniques, such as X-ray, CT and surface scanning. To provide a better understanding of their production, we examined these masks layer by layer, considering the use of a mould to shape the face from inside, the layering of textile, the application of gesso to strengthen the mask or to sculpt features, and finally the decorative layer of paints and gilding.

Error estimation on extracorporeal trajectory determination from body scans

This study explores the magnitude of two sources of error that are introduced when extracorporeal bullet trajectories are based on post-mortem computed tomography (PMCT) and/or surface scanning of a body. The first source of error is caused by an altered gravitational pull on soft tissue, which is introduced when a body is scanned in another position than it had when hit. The second source of error is introduced when scanned images are translated into a virtual representation of the victim’s body. To study the combined magnitude of these errors, virtual shooting trajectories with known vertical angles through five “victims” (live test persons) were simulated. The positions of the simulated wounds on the bodies were marked, with the victims in upright positions. Next, the victims were scanned in supine position, using 3D surface scanning, similar to a body’s position when scanned during a PMCT. Seven experts, used to working with 3D data, were asked to determine the bullet trajectories based on the virtual representations of the bodies. The errors between the known and determined trajectories were analysed and discussed. The results of this study give a feel for the magnitude of the introduced errors and can be used to reconstruct actual shooting incidents using PMCT data.

Fabrication of 3D Models for Microtia Reconstruction Using Smartphone-Based Technology

Objective: Microtia reconstruction is technically challenging due to the intricate contours of the ear. It is common practice to use a two-dimensional tracing of the patient’s normal ear as a template for the reconstruction of the affected side. Recent advances in three-dimensional (3D) surface scanning and printing have expanded the ability to create surgical models preoperatively. This study aims to describe a simple and affordable process to fabricate patient-specific 3D ear models for use in the operating room. Study design: Applied basic research on a novel 3D optical scanning and fabrication pathway for microtia reconstruction. Setting: Tertiary care university hospital. Methods: Optical surface scanning of the patient’s normal ear was completed using a smartphone with facial recognition capability. The Heges application used the phone’s camera to capture the 3D image. The 3D model was digitally isolated and mirrored using the Meshmixer software and printed with a 3D printer (MonopriceTM Select Mini V2) using polylactic acid filaments. Results: The 3D model of the ear served as a helpful intraoperative reference and an adjunct to the traditional 2D template. Collectively, time for imaging acquisition, editing, and fabrication was approximately 3.5 hours. The upfront cost was around $210, and the recurring cost was approximately $0.35 per ear model. Conclusion: A novel, low-cost approach to fabricate customized 3D models of the ear is introduced. It is feasible to create individualized 3D models using currently available consumer technology. The low barrier to entry raises the possibility for clinicians to incorporate 3D printing into various clinical applications.