Robust Automatic Measurement of 3D Scanned Models for the Human Body Fat Estimation

Giachetti, Andrea; Lovato, Christian; Piscitelli, Francesco; Milanese, Chiara; Zancanaro, Carlo

doi:10.1109/jbhi.2014.2314360

Cited by 31 publications

(19 citation statements)

References 23 publications

(19 reference statements)

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“…Children who could not stand still were sedated for this assessment. From the DXA whole body evaluation, the gold-standard method (Giachetti et al, 2015;Gurka et al, 2010;Liu et al, 2005) was established; fat mass (FM) and fat-free mass (FFM) were both presented in kilograms. Another variable that was measured was body fat percentage (BF%).…”

Section: Methodsmentioning

confidence: 99%

Mathematical model for body fat percentage of children with cerebral palsy

et al. 2017

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Introduction: The aim of this study was to develop a specific mathematical model to estimate the body fat percentage (BF%) of children with cerebral palsy, based on a Brazilian population of patients with this condition. Method: This is a descriptive cross-sectional study. The study included 63 Caucasian children with cerebral palsy, both males and females, aged between three and ten-years-old. Participants were assessed for functional motor impairment using the Gross Motor Function Classification System (GMFCS), dual energy x-ray absorptiometry (DXA) and skinfold thickness. Total body mass (TBM) and skinfolds thickness from: triceps (Tr), biceps (Bi), Suprailiac (Si), medium thigh (Th), abdominal (Ab), medial calf (Ca) and subscapular (Se) were collected. Fat mass (FM) was estimated by dual energy x-ray absorptiometry (gold standard). Results:The model was built from multivariate linear regression; FM was set as a dependent variable and other anthropometric variables, age and sex, were set as independent variables. The final model was established as F%=((0.433xTBM + 0.063xTh + 0.167xSi -6.768) ÷ TBM) × 100, the R 2 value was 0.950, R 2 adjusted =0.948 and the standard error of estimate was 1.039 kg. Conclusion: This method was shown to be valid to estimate body fat percentage of children with cerebral palsy. Also, the measurement of skinfolds on both sides of the body showed good results in this modelling.

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Section: Methodsmentioning

confidence: 99%

Mathematical model for body fat percentage of children with cerebral palsy

et al. 2017

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“…Acquired models were post processed with a Meshlab [6] script to remove outliers and floor points, remeshed with the Poisson method in order to obtain watertight models, and simplified with the quadric edge collapse method in order to reduce their complexity and get a controlled number of faces (20K). On the resulting models, a software implementing the processing pipeline described in [5] has been applied. This software estimates a curve skeleton of the model, a stick figure encoding pose, roughly segment body regions (trunk, head, limbs) and estimate a set of anthropometric measures not depending on accurate landmarking.…”

Section: Methodsmentioning

confidence: 99%

“…While using traditional anthropometry the collection of relevant morphological measurement would be extremely time consuming (and operator-depending). Our research group recently developed a method to collect automatically a set of morphological measurements that has been successfully applied for the indirect estimation of body fat and to characterize human morphotypes [5]. In this work we use this tool, together with other acquisition methods (weight measurements, anthropometry and DXA scanning) to analyze the body features of different sets of professional athletes, trying to derive peculiar statistical characteristics of each group that can be considered distinctive of the specific sports or are likely to be changed by the specific activity.…”

Section: Introductionmentioning

confidence: 99%

Automatic Analysis of 3D Scans of Professional Athletes

Giachetti¹,

Piscitelli²,

Cavedon³

et al. 2015

Proceedings of the 6th International Conference on 3D Body Scanning Technologies, Lugano, Switzerland, 27-28 October 2015

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In this paper we present an analysis of body features of professional athletes performed using 3D body scanning with automatic processing and measurement of acquired 3D meshes and body composition data from dual-energy X-ray absorptiometry (DXA) acquisition. The aim of the work was to investigate whether professional male athletes practicing different sports show sport-specific features in terms of specific body dimensions and body composition. To perform the study, we collected 3D body scans and DXA scans of 211 players practicing basketball, soccer, golf, handball, rugb, volleyball as well as a control group of 38 physically active young adults. A set of geometrical parameters were extracted automatically from the models exploiting a custom software tool based on body segmentation based curve skeleton analysis and symmetry based heuristics and previously applied with success to the analysis of body fat. By measuring these body features from the scans, we could perform statistical analysis of their correlation with body composition parameters and also analyze differences among sports, in order to understand which features are more characterizing individual sports. Furthermore, we checked if combinations of the selected feature measurements could possibly be characteristics of the disciplines and/or distinguish between professional athletes and physically active subjects, by visually analyzing the multidimensional feature space and testing automatic "athlete" or "discipline" labeling in a leave one out classification framework using different feature combinations and different classification methods. This allowed us to extract the most relevant features related to each different group.

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“…[29] used heterogeneous body scans as input data for the automatic extraction of geometrical parameters related to body fat. Their aim was the computation of parameters not dependent on the precise location of anatomical landmarks, and robust against pose and mesh quality, so as to be used in healthcare applications.…”

Section: Digital Anthropometric Measurementsmentioning

confidence: 99%

Face morphology: Can it tell us something about body weight and fat?

Pascali

Giorgi

Bastiani

et al. 2016

Computers in Biology and Medicine

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This paper proposes a method for an automatic extraction of geometric features, related to weight parameters, from 3D facial data acquired with lowcost depth scanners. The novelty of the method relies both on the processing of the 3D facial data and on the definition of the geometric features which are conceptually simple, robust against noise and pose estimation errors, computationally efficient, invariant with respect to rotation, translation, and scale changes. Experimental results show that these measurements are highly correlated with weight, BMI, and neck circumference, and well correlated with waist and hip circumference, which are markers of central obesity.Therefore the proposed method strongly supports the development of interactive, non obtrusive systems able to provide a support for the detection of weight-related problems.

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Robust Automatic Measurement of 3D Scanned Models for the Human Body Fat Estimation

Cited by 31 publications

References 23 publications

Mathematical model for body fat percentage of children with cerebral palsy

Mathematical model for body fat percentage of children with cerebral palsy

Automatic Analysis of 3D Scans of Professional Athletes

Face morphology: Can it tell us something about body weight and fat?

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