bioRxivBioengineering
Estimating body segment parameters from three-dimensional human body scans
Abstract
Body segment parameters are inputs for a range of applications. The estimation of body segment parameters that are participant-specific is desirable as it 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 camera, we developed a 3D surface scanning protocol that estimated 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 software 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 segments 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 all of the body segment parameters of interest. The work here outlines an inexpensive 3D surface scanning approach for estimating a range of participant-specific body segment parameters.
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Estimating Body Segment Properties for Adults AcrossDiverse Body Morphologies: A Data-Driven GeometricFramework
Onorato ď Angelis, Chi-Whan Choi, … Seungmoon Song·bioRxiv·Jul 6, 2026
- From Body Hulls to Musculoskeletal Models: Personalized Inertial Parameter Estimation
Markus Gambietz, Putri Qistina Azam, … Anne D. Koelewijn·PLoS ONEPeer-reviewed·Jun 24, 2026
- Estimating body volumes and surface areas of animalsfrom cross-sections
R. J. Zhao·bioRxiv·Oct 13, 2023
- Rapid bilevel optimization to concurrently solve musculoskeletal scaling, marker registration, and inverse kinematic problems for human motion reconstruction
K. Werling, M. Raitor, … C. K. Liu·bioRxiv·Aug 22, 2022
- Personalizing a computational upper body model improves kinematic tracking in high range-of-motion arm movements
Jennifer N. Maier, Nicholas A. Bianco, … Scott L. Delp·Journal of BiomechanicsPeer-reviewed·Oct 11, 2024
- AddBiomechanics: Automating model scaling, inverse kinematics, and inverse dynamics from human motion data through sequential optimization
K. Werling, N. A. Bianco, … C. K. Liu·bioRxiv·Jun 15, 2023