Statistical Human Body Form Classification: Methodology Development and Application
Type of Degreedissertation
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The focus of this exploratory study was statistical human body form classification. Prior studies have explored human body size and shape but few have explored human body form. The actual human body is a three-dimensional (3D) object and form is the construct that best represents the body. Body scanning can portray the 3D human form as a digital point cloud containing in excess of one million data points. This study intended to develop a statistical human body form classification methodology and apply that methodology to a sample of 117 male subject’s body scan data. Four (4) research questions guided the study. They were (1) Will body form categories emerge from an unsupervised hierarchical clustering of 3D male body scan data?, (2) What are the statistical characteristics of each cluster?, (3) What are the visual characteristics of each cluster?, and (4) Do experts in the field of somatology recognize the various clusters from the statistical and visual characteristics generated? The study structure consisted of a pretest (to test the statistical methodology), a clustering of male body form exercise (to answer research questions one and two), and an expert recognition of clusters (to answer research questions three and four). To answer research questions one, two and three, the methodology established in the pretest was applied to a sample of 117 male subject’s 3D body scan data. An unsupervised hierarchical classification was performed revealing seven defined clusters and answering research question one. Statistical characteristics like the number of subjects included, average age, average height, average weight, and average BMI were reported for each cluster answering research question two. Front and side view images generated by 3D body scanning were obtained for the two most extreme subject members and the median subject member in each identified cluster. These 21 images were used by a panel of experts to generate written visual characteristics for each cluster thus answering research question three. The panel of experts used answers to research questions one, two, and three to aid in their task of answering research question four. The panel did recognize the clusters generated with two exceptions concerning clusters with fewer than 5 members that could possibly be merged into adjoining clusters. The overall result of this exploratory study was the methodology was successful at generating meaningful body form clusters utilizing 3D body scan data. This study is most significant because it provides a foundational work to reduce processing time of body form classification studies using large amounts of data. Other significant contributions include the quantitative generation of meaningful body form categories from the 3D body scan data of specific samples, the statistical data reduction technique application to raw 3D body scan data, and the opportunity to collaborate with fields like kinesiology, psychology, nutrition, and statistics. Future study includes expanding the methodology to different data sets and strengthening the current analysis methodology.