Assessing human morphology using statistical shape analysis.

THELWELL, Michael (2020). Assessing human morphology using statistical shape analysis. Doctoral, Sheffield Hallam University.

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Link to published version:: https://doi.org/10.7190/shu-thesis-00369
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    Abstract

    Measurements of human body size and shape are an important source of information for a range of scientific fields and applications; however, practitioners still rely on traditional tools and methods which limit the kinds of measurements that can be taken. Recent literature has suggested that 3D imaging technology is a more sophisticated tool that could enable the comprehensive characterisation of human body shape. The aim of this programme of doctoral study was to determine whether shape anthropometrics can complement existing techniques in the assessment of human morphology. A novel analytical procedure was developed using geometric morphometrics and statistical shape analysis methods to extract numeric parameters from 3D imaging data, which describe scale-invariant characteristics of human torso shape. Though errors in anatomical landmark identification and participant scanning posture can affect the acquisition of shape anthropometrics, the developed methods were found to have high test-retest reliability, suitable for use within subsequent investigations. A series of investigations were conducted to determine whether shape measures provide additional information which is not captured by existing anthropometric techniques. The findings of these investigations suggest that body shape measures show a complex dependence on body size. Though certain shape features demonstrate a degree of allometric scaling and change with increases in body size, there are significant proportions of shape variation which cannot be explained by existing anthropometrics. These non-allometric variations in body shape have been shown to improve the estimation of subcutaneous abdominal adiposity in a small cohort of participants, and have demonstrated the potential for misclassification of individuals using existing indices, such as BMI and WHR. This programme of research provides a more detailed understanding of human morphological variation, which could inform the development of improved tools for characterising how body shape relates to its underlying mass distribution.

    Item Type: Thesis (Doctoral)
    Additional Information: Director of Studies - Dr. Simon Choppin Supervisors - Professor Jon Wheat, Dr. John Hart, Dr. Alice Bullas.
    Research Institute, Centre or Group - Does NOT include content added after October 2018: Sheffield Hallam Doctoral Theses
    Identification Number: https://doi.org/10.7190/shu-thesis-00369
    Depositing User: Justine Gavin
    Date Deposited: 09 Jun 2021 11:38
    Last Modified: 09 Jun 2021 11:45
    URI: http://shura.shu.ac.uk/id/eprint/28734

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