ArticlePloS one2022
Estimating body segment parameters from three-dimensional human body scans.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Development of a pelvic musculoskeletal model based on statistical shape modelling for estimating sacroiliac and pubic joint reaction forces in females.Biomechanics and modeling in mechanobiology · 2026Article
- A compact external-rail linear actuation mechanism with a high stroke-to-length ratio for wearable exoskeleton applications.Frontiers in robotics and AI · 2026Article
- Center of Mass (CoM) Motions and Foot Placement During Treadmill Walking Using One Time-of-Flight Camera.Sensors (Basel, Switzerland) · 2025Article
- Measurement of Human Body Segment Properties Using Low-Cost RGB-D Cameras.Sensors (Basel, Switzerland) · 2025Article
- Subject-specific trunk segmental masses prediction for musculoskeletal models using artificial neural networks.Medical & biological engineering & computing · 2024Article
- Feasibility of Using Laser Imaging Detection and Ranging Technology for Contactless 3D Body Scanning and Anthropometric Assessment of Athletes.Sports (Basel, Switzerland) · 2024Article
- System for Estimation of Human Anthropometric Parameters Based on Data from Kinect v2 Depth Camera.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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