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

Estimation of Body Segment Inertia Parameters (BSIP) is a necessary step to perform dynamic analysis of human movement. As BSIPs cannot be directly measured, they are usually estimated using regressions derived from anthropometric tables (AT, e.g. Dumas et al. 2007). However, these tables are usually not adapted to atypical populations (children, elderly, obese, individuals with prostheses, etc.) that are classically of interest. Another option consists in estimating segments’ volumes and in deriving their BSIPs using assumptions on their density (usually a constant uniform density). 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. segmented and projected into local coordinate systems (LCS) according to anatomical definition and landmarks (e.g. Wu et al., 2005). Thus, this study aims at proposing a semi-automatic method (i.e. with minimal manual intervention) to estimate relevant BSIP from body scanner images. It is specifically dedicated to be used in the context a human movement analysis framework.