Prediction of human male trunk mass distribution using anthropometric measurements: A feasibility study

Tao Liu, Kinda Khalaf, Nader Hebela, Lindsey Westover, Fabio Galbusera, Marwan El-Rich

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

The current study proposes a new method to predict the body shape and mass distribution of the trunk (Tl-L5) of a human male using 15 anthropometric measurements acquired at various locations of the body. Trunk cross-sectional images adopted from the Visible Human male project database were segmented into fat, bone, and lean tissue. Assuming that all male subjects have similar cross-sectional composition at a given body height percentile, areas of the segmented cross-sectional images of the Visible Human male along the trunk were scaled to match those of the predicted body shape. The trunk mass distribution of the target subject can then be computed using the density values of fat, bone, and lean tissue. Comparison of the predicted body shape circumference with ground truth values measured using digital and actual measurements yielded maximum mean error of 13.3 mm and 30.3 mm, respectively. The accuracy of the image segmentation was evaluated, and the results showed a high Jaccard index (>0.95). The proposed method was able to predict the trunk mass distribution of two volunteers with a maximum deviation of 384 g at T4 level and a minimum deviation of 12 g at L4 level and the corresponding centers of mass fell within the experimental data at most levels. Thus, our method can be considered as a feasible option to calculate subject-specific trunk mass distribution.

Original languageBritish English
Article number110437
JournalJournal of Biomechanics
Volume122
DOIs
StatePublished - 9 Jun 2021

Keywords

  • Conventional neural network
  • Human body shape
  • Image segmentation
  • Principal component analysis
  • Trunk mass distribution

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