Modelling of human torso shape variation inferred by geometric morphometrics

THELWELL, Michael, BULLAS, Alice, KÜHNAPFEL, Andreas, HART, John, AHNERT, Peter, WHEAT, Jonathan, LOEFFLER, Markus, SCHOLZ, Markus and CHOPPIN, Simon (2022). Modelling of human torso shape variation inferred by geometric morphometrics. PLOS ONE, 17 (3).

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Official URL: https://journals.plos.org/plosone/article?id=10.13...
Open Access URL: https://journals.plos.org/plosone/article/file?id=... (Published version)
Link to published version:: https://doi.org/10.1371/journal.pone.0265255
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    Abstract

    Traditional body measurement techniques are commonly used to assess physical health; however, these approaches do not fully represent the complex shape of the human body. Three-dimensional (3D) imaging systems capture rich point cloud data that provides a representation of the surface of 3D objects and have been shown to be a potential anthropometric tool for use within health applications. Previous studies utilising 3D imaging have only assessed body shape based on combinations and relative proportions of traditional body measures, such as lengths, widths and girths. Geometric morphometrics (GM) is an established framework used for the statistical analysis of biological shape variation. These methods quantify biological shape variation after the effects of non-shape variation–location, rotation and scale–have been mathematically held constant, otherwise known as the Procrustes paradigm. The aim of this study was to determine whether shape measures, identified using geometric morphometrics, can provide additional information about the complexity of human morphology and underlying mass distribution compared to traditional body measures. Scale-invariant features of torso shape were extracted from 3D imaging data of 9,209 participants form the LIFE-Adult study. Partial least squares regression (PLSR) models were created to determine the extent to which variations in human torso shape are explained by existing techniques. The results of this investigation suggest that linear combinations of body measures can explain 49.92% and 47.46% of the total variation in male and female body shape features, respectively. However, there are also significant amounts of variation in human morphology which cannot be identified by current methods. These results indicate that Geometric morphometric methods can identify measures of human body shape which provide complementary information about the human body. The aim of future studies will be to investigate the utility of these measures in clinical epidemiology and the assessment of health risk.

    Item Type: Article
    Additional Information: ** From PLOS via Jisc Publications Router ** Licence for this article: http://creativecommons.org/licenses/by/4.0/ **Journal IDs: eissn 1932-6203 **Article IDs: publisher-id: pone-d-21-30993 **History: published_online 10-03-2022; accepted 26-02-2022; collection 2022; submitted 25-09-2021
    Uncontrolled Keywords: Research Article, Biology and life sciences, Medicine and health sciences, Research and analysis methods, Physical sciences
    Identification Number: https://doi.org/10.1371/journal.pone.0265255
    SWORD Depositor: Colin Knott
    Depositing User: Colin Knott
    Date Deposited: 14 Mar 2022 13:06
    Last Modified: 14 Mar 2022 13:06
    URI: http://shura.shu.ac.uk/id/eprint/29879

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