THELWELL, Michael (2020). Assessing human morphology using statistical shape analysis. Doctoral, Sheffield Hallam University.
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Thelwell_2021_PhD_AssessingHumanMorphology.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
Thelwell_2021_PhD_AssessingHumanMorphology.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.
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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.
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