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

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. These quantities are, however, 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 multicamera 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.

Interaction between an operator and the control desk at the control room of the railway traffic: A Serbian experience

“…The values for the segmental masses of the body as parts of the total body mass of the body of the operator M are: the mass of body and head m bh = 0.58· M (kg); the mass of the upper arm m ua = 0.030· M (kg); the mass of the forearm m fa = 0.018· M (kg); the mass of the palm m p = 0.006· M (kg); the mass of the thigh m th = 0.094· M (kg); the mass of the lower leg m ll = 0.042· M (kg); the mass of the foot m fo = 0.02· M (kg) (Bezuidenhout & Domleo, 2013; Chang, Chablat, Ma, & Fouad, 2018; Clarkson, Wheat, Heller, & Choppin, 2014; Kudzia, 2015; Nikolova, 2010; Peyer, Morris, & Sellers, 2015).…”

A quantitative evaluation of physical and digital approaches to centre of mass estimation

“…; Peyer et al. ). Digital models offer some advantages over physical methods including ease of data sharing and simple manipulation for sensitivity analyses and repeatability analyses, in addition to the advantages of scanning procedures such as computed tomography (CT).…”

Estimation of body segment inertia parameters from 3D body scanner images: a semi-automatic method dedicated to human movement analysis applications

“…This option is of growing interest as the developments of low-cost 3D scanners yield to simple and accessible ways to obtain 3D body shapes (e.g. Peyer et al 2015). However, there are still some issues to transform the measured external envelop into 3D shapes for each segment that are relevant for the human movement analysis, i.e.…”